Overview

Dataset statistics

Number of variables44
Number of observations6575956
Missing cells1802992
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 GiB
Average record size in memory352.0 B

Variable types

Numeric17
Categorical27

Alerts

CAE_ESTADO has constant value "1.0"Constant
POS_FECHA_POSTULACION has a high cardinality: 601550 distinct valuesHigh cardinality
IES_NOMBRE_INSTIT has a high cardinality: 245 distinct valuesHigh cardinality
CAR_NOMBRE_CARRERA has a high cardinality: 410 distinct valuesHigh cardinality
CANTON has a high cardinality: 78 distinct valuesHigh cardinality
PARROQUIA has a high cardinality: 113 distinct valuesHigh cardinality
CAM_NOMBRE_CAMPUS has a high cardinality: 175 distinct valuesHigh cardinality
Unnamed: 0 is highly overall correlated with PRD_ID_NUM_POSTULACIONHigh correlation
INS_ID is highly overall correlated with INI_ID and 8 other fieldsHigh correlation
INI_ID is highly overall correlated with INS_ID and 8 other fieldsHigh correlation
CAE_NOTA_POSTULA is highly overall correlated with NOTA_POSTULAHigh correlation
POS_ID is highly overall correlated with INS_ID and 7 other fieldsHigh correlation
CUS_ID is highly overall correlated with INS_ID and 7 other fieldsHigh correlation
NOTA_POSTULA is highly overall correlated with CAE_NOTA_POSTULAHigh correlation
PRD_ID_NUM_POSTULACION is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
IES_ID is highly overall correlated with IES_TIPO_IESHigh correlation
OFA_ID is highly overall correlated with INS_ID and 7 other fieldsHigh correlation
APC_ID is highly overall correlated with INS_ID and 7 other fieldsHigh correlation
CCP_ID is highly overall correlated with INS_ID and 7 other fieldsHigh correlation
CAR_ID is highly overall correlated with IES_TIPO_IES and 1 other fieldsHigh correlation
MODALIDAD_ID is highly overall correlated with MODALIDAD and 2 other fieldsHigh correlation
AREA_ID is highly overall correlated with AREA_NOMBRE and 1 other fieldsHigh correlation
SUBAREA_ID is highly overall correlated with AREA_NOMBRE and 1 other fieldsHigh correlation
PER_ID is highly overall correlated with INS_ID and 8 other fieldsHigh correlation
INS_POBLACION is highly overall correlated with INS_TIPO_INSCRIPCIONHigh correlation
INS_TIPO_INSCRIPCION is highly overall correlated with INS_ID and 2 other fieldsHigh correlation
SEGMENTO_ASPIRANTE is highly overall correlated with CAE_GRUPOHigh correlation
CAE_GRUPO is highly overall correlated with PER_ID and 1 other fieldsHigh correlation
IES_TIPO_IES is highly overall correlated with IES_ID and 2 other fieldsHigh correlation
IES_TIPO_FINANCIAMIENTO is highly overall correlated with PRD_ID_SEGMENTO and 1 other fieldsHigh correlation
MODALIDAD is highly overall correlated with MODALIDAD_ID and 2 other fieldsHigh correlation
JORNADA_ID is highly overall correlated with MODALIDAD_ID and 3 other fieldsHigh correlation
JORNADA is highly overall correlated with MODALIDAD_ID and 3 other fieldsHigh correlation
NIVEL is highly overall correlated with IES_TIPO_IESHigh correlation
AREA_NOMBRE is highly overall correlated with AREA_ID and 2 other fieldsHigh correlation
SUBAREA_NOMBRE is highly overall correlated with CAR_ID and 3 other fieldsHigh correlation
PROVINCIA is highly overall correlated with CANTONHigh correlation
CANTON is highly overall correlated with JORNADA_ID and 2 other fieldsHigh correlation
PRD_ID_SEGMENTO is highly overall correlated with IES_TIPO_FINANCIAMIENTO and 1 other fieldsHigh correlation
SEGMETO_CARRERA is highly overall correlated with IES_TIPO_FINANCIAMIENTO and 2 other fieldsHigh correlation
archivo is highly overall correlated with INS_ID and 9 other fieldsHigh correlation
SEGMENTO_ASPIRANTE is highly imbalanced (56.6%)Imbalance
CAE_GRUPO is highly imbalanced (61.8%)Imbalance
POS_ESTADO is highly imbalanced (> 99.9%)Imbalance
IES_TIPO_IES is highly imbalanced (59.1%)Imbalance
IES_TIPO_FINANCIAMIENTO is highly imbalanced (89.4%)Imbalance
IES_ESTADO is highly imbalanced (99.9%)Imbalance
MODALIDAD is highly imbalanced (66.3%)Imbalance
NIVEL is highly imbalanced (78.2%)Imbalance
PRD_ID_SEGMENTO is highly imbalanced (90.4%)Imbalance
SEGMETO_CARRERA is highly imbalanced (93.4%)Imbalance
INS_POBLACION has 1390432 (21.1%) missing valuesMissing

Reproduction

Analysis started2023-03-10 07:31:37.575840
Analysis finished2023-03-10 07:46:15.119031
Duration14 minutes and 37.54 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct861282
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267908.12
Minimum1
Maximum861282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:15.199413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19453
Q1101652
median217683
Q3394023
95-th percentile670846
Maximum861282
Range861281
Interquartile range (IQR)292371

Descriptive statistics

Standard deviation204594.32
Coefficient of variation (CV)0.76367344
Kurtosis-0.30705975
Mean267908.12
Median Absolute Deviation (MAD)134983
Skewness0.77808933
Sum1.761752 × 1012
Variance4.1858834 × 1010
MonotonicityNot monotonic
2023-03-10T02:46:15.552137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 21
 
< 0.1%
461 21
 
< 0.1%
453 21
 
< 0.1%
454 21
 
< 0.1%
455 21
 
< 0.1%
456 21
 
< 0.1%
457 21
 
< 0.1%
458 21
 
< 0.1%
459 21
 
< 0.1%
460 21
 
< 0.1%
Other values (861272) 6575746
> 99.9%
ValueCountFrequency (%)
1 21
< 0.1%
2 21
< 0.1%
3 21
< 0.1%
4 21
< 0.1%
5 21
< 0.1%
6 21
< 0.1%
7 21
< 0.1%
8 21
< 0.1%
9 21
< 0.1%
10 21
< 0.1%
ValueCountFrequency (%)
861282 1
< 0.1%
861281 1
< 0.1%
861280 1
< 0.1%
861279 1
< 0.1%
861278 1
< 0.1%
861277 1
< 0.1%
861276 1
< 0.1%
861275 1
< 0.1%
861274 1
< 0.1%
861273 1
< 0.1%

INS_ID
Real number (ℝ)

Distinct970233
Distinct (%)14.8%
Missing4317
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9631397.1
Minimum7006528
Maximum12266030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:15.629167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7006528
5-th percentile7185591
Q18275071
median9476282
Q311358329
95-th percentile11892310
Maximum12266030
Range5259502
Interquartile range (IQR)3083258

Descriptive statistics

Standard deviation1553580.3
Coefficient of variation (CV)0.16130373
Kurtosis-1.2437661
Mean9631397.1
Median Absolute Deviation (MAD)1313842
Skewness0.016919285
Sum6.3294065 × 1013
Variance2.4136117 × 1012
MonotonicityNot monotonic
2023-03-10T02:46:15.690040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12263583 18
 
< 0.1%
11404388 18
 
< 0.1%
11421690 18
 
< 0.1%
11861922 18
 
< 0.1%
11696132 18
 
< 0.1%
11588166 18
 
< 0.1%
11995611 18
 
< 0.1%
12037948 18
 
< 0.1%
11733932 18
 
< 0.1%
11618976 18
 
< 0.1%
Other values (970223) 6571459
99.9%
(Missing) 4317
 
0.1%
ValueCountFrequency (%)
7006528 5
 
< 0.1%
7006530 5
 
< 0.1%
7006532 15
< 0.1%
7006534 3
 
< 0.1%
7006536 7
< 0.1%
7006538 15
< 0.1%
7006540 3
 
< 0.1%
7006542 10
< 0.1%
7006544 5
 
< 0.1%
7006546 5
 
< 0.1%
ValueCountFrequency (%)
12266030 7
 
< 0.1%
12265994 5
 
< 0.1%
12265986 18
< 0.1%
12265981 3
 
< 0.1%
12265977 9
< 0.1%
12265974 5
 
< 0.1%
12265962 5
 
< 0.1%
12265961 5
 
< 0.1%
12265960 2
 
< 0.1%
12265959 5
 
< 0.1%

INI_ID
Real number (ℝ)

Distinct970231
Distinct (%)14.8%
Missing4317
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5715046.1
Minimum3941098
Maximum7723593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:15.757611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3941098
5-th percentile4030626
Q14803508
median5622128
Q36794408
95-th percentile7068742
Maximum7723593
Range3782495
Interquartile range (IQR)1990900

Descriptive statistics

Standard deviation1029845.3
Coefficient of variation (CV)0.18019896
Kurtosis-1.1716456
Mean5715046.1
Median Absolute Deviation (MAD)856436
Skewness-0.092666549
Sum3.755722 × 1013
Variance1.0605814 × 1012
MonotonicityNot monotonic
2023-03-10T02:46:15.817171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7459572 18
 
< 0.1%
6877165 18
 
< 0.1%
6833413 18
 
< 0.1%
7053573 18
 
< 0.1%
6970637 18
 
< 0.1%
6916683 18
 
< 0.1%
7422449 18
 
< 0.1%
7479712 18
 
< 0.1%
6989531 18
 
< 0.1%
6932056 18
 
< 0.1%
Other values (970221) 6571459
99.9%
(Missing) 4317
 
0.1%
ValueCountFrequency (%)
3941098 5
 
< 0.1%
3941099 5
 
< 0.1%
3941100 15
< 0.1%
3941101 3
 
< 0.1%
3941102 7
< 0.1%
3941103 15
< 0.1%
3941104 3
 
< 0.1%
3941105 10
< 0.1%
3941106 5
 
< 0.1%
3941107 5
 
< 0.1%
ValueCountFrequency (%)
7723593 7
< 0.1%
7723589 5
 
< 0.1%
7723588 15
< 0.1%
7723585 3
 
< 0.1%
7723584 6
 
< 0.1%
7723581 9
< 0.1%
7723580 5
 
< 0.1%
7723575 9
< 0.1%
7723571 5
 
< 0.1%
7723570 13
< 0.1%

PER_ID
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
22
1620447 
19
1401656 
21
1308865 
20
1200308 
18
1044680 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters13151912
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 1620447
24.6%
19 1401656
21.3%
21 1308865
19.9%
20 1200308
18.3%
18 1044680
15.9%

Length

2023-03-10T02:46:15.871124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:15.928023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
22 1620447
24.6%
19 1401656
21.3%
21 1308865
19.9%
20 1200308
18.3%
18 1044680
15.9%

Most occurring characters

ValueCountFrequency (%)
2 5750067
43.7%
1 3755201
28.6%
9 1401656
 
10.7%
0 1200308
 
9.1%
8 1044680
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13151912
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5750067
43.7%
1 3755201
28.6%
9 1401656
 
10.7%
0 1200308
 
9.1%
8 1044680
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 13151912
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5750067
43.7%
1 3755201
28.6%
9 1401656
 
10.7%
0 1200308
 
9.1%
8 1044680
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13151912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5750067
43.7%
1 3755201
28.6%
9 1401656
 
10.7%
0 1200308
 
9.1%
8 1044680
 
7.9%

INS_POBLACION
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing1390432
Missing (%)21.1%
Memory size50.2 MiB
No escolar
3233246 
Escolar
1951907 
Escolar rezagado
 
371

Length

Max length16
Median length10
Mean length8.8711854
Min length7

Characters and Unicode

Total characters46001745
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo escolar
2nd rowNo escolar
3rd rowNo escolar
4th rowNo escolar
5th rowNo escolar

Common Values

ValueCountFrequency (%)
No escolar 3233246
49.2%
Escolar 1951907
29.7%
Escolar rezagado 371
 
< 0.1%
(Missing) 1390432
21.1%

Length

2023-03-10T02:46:15.976158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:16.023923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
escolar 5185524
61.6%
no 3233246
38.4%
rezagado 371
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 8419141
18.3%
a 5186266
11.3%
r 5185895
11.3%
s 5185524
11.3%
c 5185524
11.3%
l 5185524
11.3%
3233617
 
7.0%
e 3233617
 
7.0%
N 3233246
 
7.0%
E 1952278
 
4.2%
Other values (3) 1113
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37582604
81.7%
Uppercase Letter 5185524
 
11.3%
Space Separator 3233617
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8419141
22.4%
a 5186266
13.8%
r 5185895
13.8%
s 5185524
13.8%
c 5185524
13.8%
l 5185524
13.8%
e 3233617
 
8.6%
z 371
 
< 0.1%
g 371
 
< 0.1%
d 371
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 3233246
62.4%
E 1952278
37.6%
Space Separator
ValueCountFrequency (%)
3233617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42768128
93.0%
Common 3233617
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8419141
19.7%
a 5186266
12.1%
r 5185895
12.1%
s 5185524
12.1%
c 5185524
12.1%
l 5185524
12.1%
e 3233617
 
7.6%
N 3233246
 
7.6%
E 1952278
 
4.6%
z 371
 
< 0.1%
Other values (2) 742
 
< 0.1%
Common
ValueCountFrequency (%)
3233617
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46001745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8419141
18.3%
a 5186266
11.3%
r 5185895
11.3%
s 5185524
11.3%
c 5185524
11.3%
l 5185524
11.3%
3233617
 
7.0%
e 3233617
 
7.0%
N 3233246
 
7.0%
E 1952278
 
4.2%
Other values (3) 1113
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
1.0
5258059 
3.0
1300335 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19675182
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 5258059
80.0%
3.0 1300335
 
19.8%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:16.066117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:16.111033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5258059
80.2%
3.0 1300335
 
19.8%

Most occurring characters

ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 5258059
26.7%
3 1300335
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13116788
66.7%
Other Punctuation 6558394
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6558394
50.0%
1 5258059
40.1%
3 1300335
 
9.9%
Other Punctuation
ValueCountFrequency (%)
. 6558394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19675182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 5258059
26.7%
3 1300335
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19675182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 5258059
26.7%
3 1300335
 
6.6%

SEGMENTO_ASPIRANTE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
POBLACION GENERAL
4006717 
POLITICA DE ACCION AFIRMATIVA
2520124 
IES PARTICULAR
 
22513
MERITO TERRITORIAL
 
6440
GAR
 
2600

Length

Max length29
Median length17
Mean length21.596246
Min length3

Characters and Unicode

Total characters141636687
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOBLACION GENERAL
2nd rowPOBLACION GENERAL
3rd rowPOBLACION GENERAL
4th rowPOBLACION GENERAL
5th rowPOBLACION GENERAL

Common Values

ValueCountFrequency (%)
POBLACION GENERAL 4006717
60.9%
POLITICA DE ACCION AFIRMATIVA 2520124
38.3%
IES PARTICULAR 22513
 
0.3%
MERITO TERRITORIAL 6440
 
0.1%
GAR 2600
 
< 0.1%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:16.152614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:16.205154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
poblacion 4006717
22.1%
general 4006717
22.1%
politica 2520124
13.9%
de 2520124
13.9%
accion 2520124
13.9%
afirmativa 2520124
13.9%
ies 22513
 
0.1%
particular 22513
 
0.1%
merito 6440
 
< 0.1%
territorial 6440
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 20668120
14.6%
I 16671683
11.8%
O 13066562
9.2%
11596042
8.2%
C 11589602
8.2%
E 10568951
7.5%
L 10562511
7.5%
N 10533558
7.4%
R 6600227
 
4.7%
P 6549354
 
4.6%
Other values (9) 23230077
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 130040645
91.8%
Space Separator 11596042
 
8.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 20668120
15.9%
I 16671683
12.8%
O 13066562
10.0%
C 11589602
8.9%
E 10568951
8.1%
L 10562511
8.1%
N 10533558
8.1%
R 6600227
 
5.1%
P 6549354
 
5.0%
T 5082081
 
3.9%
Other values (8) 18147996
14.0%
Space Separator
ValueCountFrequency (%)
11596042
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130040645
91.8%
Common 11596042
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 20668120
15.9%
I 16671683
12.8%
O 13066562
10.0%
C 11589602
8.9%
E 10568951
8.1%
L 10562511
8.1%
N 10533558
8.1%
R 6600227
 
5.1%
P 6549354
 
5.0%
T 5082081
 
3.9%
Other values (8) 18147996
14.0%
Common
ValueCountFrequency (%)
11596042
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141636687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 20668120
14.6%
I 16671683
11.8%
O 13066562
9.2%
11596042
8.2%
C 11589602
8.2%
E 10568951
7.5%
L 10562511
7.5%
N 10533558
7.4%
R 6600227
 
4.7%
P 6549354
 
4.6%
Other values (9) 23230077
16.4%

CAE_GRUPO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
POBLACION GENERAL
3333684 
/POLITICA DE ACCION AFIRMATIVA
2167099 
/POBLACION GENERAL
681162 
POLITICA DE ACCION AFIRMATIVA
356294 
/MERITO TERRITORIAL/POLITICA DE ACCION AFIRMATIVA
 
7799
Other values (14)
 
12356

Length

Max length53
Median length17
Mean length22.099962
Min length3

Characters and Unicode

Total characters144940255
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOBLACION GENERAL
2nd rowPOBLACION GENERAL
3rd rowPOBLACION GENERAL
4th rowPOBLACION GENERAL
5th rowPOBLACION GENERAL

Common Values

ValueCountFrequency (%)
POBLACION GENERAL 3333684
50.7%
/POLITICA DE ACCION AFIRMATIVA 2167099
33.0%
/POBLACION GENERAL 681162
 
10.4%
POLITICA DE ACCION AFIRMATIVA 356294
 
5.4%
/MERITO TERRITORIAL/POLITICA DE ACCION AFIRMATIVA 7799
 
0.1%
/MERITO TERRITORIAL 6440
 
0.1%
POLITICA DE ACCION AFIRMATIVA/MERITO TERRITORIAL 2220
 
< 0.1%
GAR 1730
 
< 0.1%
GAR/POLITICA DE ACCION AFIRMATIVA 783
 
< 0.1%
/GAR 698
 
< 0.1%
Other values (9) 485
 
< 0.1%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:16.257709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
poblacion 4014846
22.1%
general 4014846
22.1%
de 2534508
13.9%
accion 2534508
13.9%
afirmativa 2532147
13.9%
politica 2525754
13.9%
merito 14239
 
0.1%
territorial 8833
 
< 0.1%
territorial/politica 7888
 
< 0.1%
gar 2444
 
< 0.1%
Other values (7) 3508
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 20722667
14.3%
I 16737585
11.5%
O 13132204
9.1%
11635127
8.0%
C 11618370
8.0%
E 10597696
7.3%
L 10580921
7.3%
N 10564218
7.3%
R 6619988
 
4.6%
P 6549372
 
4.5%
Other values (9) 26182107
18.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 130430413
90.0%
Space Separator 11635127
 
8.0%
Other Punctuation 2874715
 
2.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 20722667
15.9%
I 16737585
12.8%
O 13132204
10.1%
C 11618370
8.9%
E 10597696
8.1%
L 10580921
8.1%
N 10564218
8.1%
R 6619988
 
5.1%
P 6549372
 
5.0%
T 5119197
 
3.9%
Other values (7) 18188195
13.9%
Space Separator
ValueCountFrequency (%)
11635127
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2874715
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130430413
90.0%
Common 14509842
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 20722667
15.9%
I 16737585
12.8%
O 13132204
10.1%
C 11618370
8.9%
E 10597696
8.1%
L 10580921
8.1%
N 10564218
8.1%
R 6619988
 
5.1%
P 6549372
 
5.0%
T 5119197
 
3.9%
Other values (7) 18188195
13.9%
Common
ValueCountFrequency (%)
11635127
80.2%
/ 2874715
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144940255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 20722667
14.3%
I 16737585
11.5%
O 13132204
9.1%
11635127
8.0%
C 11618370
8.0%
E 10597696
7.3%
L 10580921
7.3%
N 10564218
7.3%
R 6619988
 
4.6%
P 6549372
 
4.5%
Other values (9) 26182107
18.1%

CAE_ESTADO
Categorical

Distinct1
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
1.0
6558394 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19675182
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6558394
99.7%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:16.301398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:16.346450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6558394
100.0%

Most occurring characters

ValueCountFrequency (%)
1 6558394
33.3%
. 6558394
33.3%
0 6558394
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13116788
66.7%
Other Punctuation 6558394
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6558394
50.0%
0 6558394
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6558394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19675182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6558394
33.3%
. 6558394
33.3%
0 6558394
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19675182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6558394
33.3%
. 6558394
33.3%
0 6558394
33.3%

CAE_NOTA_POSTULA
Real number (ℝ)

Distinct477
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean759.91985
Minimum400
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:16.390209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile661
Q1715
median755
Q3801
95-th percentile877
Maximum1000
Range600
Interquartile range (IQR)86

Descriptive statistics

Standard deviation65.120012
Coefficient of variation (CV)0.085693264
Kurtosis0.10422777
Mean759.91985
Median Absolute Deviation (MAD)43
Skewness0.36177401
Sum4.9838538 × 109
Variance4240.616
MonotonicityNot monotonic
2023-03-10T02:46:16.448083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
742 43316
 
0.7%
750 43031
 
0.7%
747 42994
 
0.7%
733 42922
 
0.7%
741 42803
 
0.7%
735 42661
 
0.6%
753 42661
 
0.6%
743 42598
 
0.6%
746 42550
 
0.6%
745 42542
 
0.6%
Other values (467) 6130316
93.2%
ValueCountFrequency (%)
400 5
 
< 0.1%
466 5
 
< 0.1%
478 5
 
< 0.1%
487 5
 
< 0.1%
499 4
 
< 0.1%
518 7
 
< 0.1%
520 23
< 0.1%
525 10
< 0.1%
529 15
< 0.1%
530 5
 
< 0.1%
ValueCountFrequency (%)
1000 215
< 0.1%
999 35
 
< 0.1%
998 37
 
< 0.1%
997 44
 
< 0.1%
996 70
 
< 0.1%
995 85
 
< 0.1%
994 69
 
< 0.1%
993 119
< 0.1%
992 107
< 0.1%
991 174
< 0.1%

POS_ID
Real number (ℝ)

Distinct6575921
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23008348
Minimum16425446
Maximum29581641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:16.542839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16425446
5-th percentile17083586
Q119726792
median23011160
Q326295474
95-th percentile28924030
Maximum29581641
Range13156195
Interquartile range (IQR)6568683

Descriptive statistics

Standard deviation3796002.2
Coefficient of variation (CV)0.16498369
Kurtosis-1.1980221
Mean23008348
Median Absolute Deviation (MAD)3284341.5
Skewness-0.0019412153
Sum1.5130188 × 1014
Variance1.4409632 × 1013
MonotonicityNot monotonic
2023-03-10T02:46:16.607816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24000000 8
 
< 0.1%
20000000 6
 
< 0.1%
21000000 6
 
< 0.1%
28000000 6
 
< 0.1%
27000000 5
 
< 0.1%
25000000 5
 
< 0.1%
19000000 4
 
< 0.1%
26000000 3
 
< 0.1%
29103938 1
 
< 0.1%
24198419 1
 
< 0.1%
Other values (6575911) 6575911
> 99.9%
ValueCountFrequency (%)
16425446 1
< 0.1%
16425447 1
< 0.1%
16425448 1
< 0.1%
16425449 1
< 0.1%
16425450 1
< 0.1%
16425453 1
< 0.1%
16425454 1
< 0.1%
16425460 1
< 0.1%
16425461 1
< 0.1%
16425462 1
< 0.1%
ValueCountFrequency (%)
29581641 1
< 0.1%
29581640 1
< 0.1%
29581631 1
< 0.1%
29581630 1
< 0.1%
29581629 1
< 0.1%
29581625 1
< 0.1%
29581624 1
< 0.1%
29581621 1
< 0.1%
29581620 1
< 0.1%
29581619 1
< 0.1%
Distinct601550
Distinct (%)9.2%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
08/08/2019
 
312910
19/11/2020
 
124783
09/08/2019
 
117099
10/08/2019
 
87169
11/08/2019
 
68004
Other values (601545)
5848429 

Length

Max length18
Median length17
Mean length16.291171
Min length10

Characters and Unicode

Total characters106843918
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29536 ?
Unique (%)0.5%

Sample

1st row27/10/2021 15:27
2nd row27/10/2021 15:27
3rd row27/10/2021 15:27
4th row27/10/2021 15:27
5th row27/10/2021 15:27

Common Values

ValueCountFrequency (%)
08/08/2019 312910
 
4.8%
19/11/2020 124783
 
1.9%
09/08/2019 117099
 
1.8%
10/08/2019 87169
 
1.3%
11/08/2019 68004
 
1.0%
20/11/2020 36435
 
0.6%
23/10/2020 23:26 1595
 
< 0.1%
23/10/2020 23:30 1471
 
< 0.1%
23/10/2020 20:49 1401
 
< 0.1%
23/10/2020 21:03 1383
 
< 0.1%
Other values (601540) 5806144
88.3%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:16.704151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19/4/2021 609459
 
4.9%
29/9/2021 551833
 
4.5%
13/3/2020 509857
 
4.1%
24/10/2020 468356
 
3.8%
19/5/2020 323269
 
2.6%
08/08/2019 312910
 
2.5%
02/05/2021 297627
 
2.4%
14/10/2021 286534
 
2.3%
6/11/2020 271124
 
2.2%
6/6/2020 184581
 
1.5%
Other values (86457) 8554838
69.2%

Most occurring characters

ValueCountFrequency (%)
2 18816626
17.6%
0 16370052
15.3%
1 16228563
15.2%
/ 13116788
12.3%
: 9919166
9.3%
5811994
 
5.4%
9 5462684
 
5.1%
3 5155482
 
4.8%
4 4738674
 
4.4%
5 4361762
 
4.1%
Other values (3) 6862127
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77995970
73.0%
Other Punctuation 23035954
 
21.6%
Space Separator 5811994
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18816626
24.1%
0 16370052
21.0%
1 16228563
20.8%
9 5462684
 
7.0%
3 5155482
 
6.6%
4 4738674
 
6.1%
5 4361762
 
5.6%
8 2844314
 
3.6%
6 2234719
 
2.9%
7 1783094
 
2.3%
Other Punctuation
ValueCountFrequency (%)
/ 13116788
56.9%
: 9919166
43.1%
Space Separator
ValueCountFrequency (%)
5811994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106843918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 18816626
17.6%
0 16370052
15.3%
1 16228563
15.2%
/ 13116788
12.3%
: 9919166
9.3%
5811994
 
5.4%
9 5462684
 
5.1%
3 5155482
 
4.8%
4 4738674
 
4.4%
5 4361762
 
4.1%
Other values (3) 6862127
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106843918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18816626
17.6%
0 16370052
15.3%
1 16228563
15.2%
/ 13116788
12.3%
: 9919166
9.3%
5811994
 
5.4%
9 5462684
 
5.1%
3 5155482
 
4.8%
4 4738674
 
4.4%
5 4361762
 
4.1%
Other values (3) 6862127
 
6.4%

CUS_ID
Real number (ℝ)

Distinct25164
Distinct (%)0.4%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean290284.95
Minimum267051
Maximum313609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:16.760881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum267051
5-th percentile267979
Q1277658
median290603
Q3303116
95-th percentile311712
Maximum313609
Range46558
Interquartile range (IQR)25458

Descriptive statistics

Standard deviation13340.978
Coefficient of variation (CV)0.045958215
Kurtosis-1.1164819
Mean290284.95
Median Absolute Deviation (MAD)12935
Skewness-0.053459198
Sum1.9038031 × 1012
Variance1.7798169 × 108
MonotonicityNot monotonic
2023-03-10T02:46:16.819187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304787 9436
 
0.1%
304597 9257
 
0.1%
304825 8232
 
0.1%
304791 7545
 
0.1%
294999 6951
 
0.1%
295018 6836
 
0.1%
304779 6301
 
0.1%
277665 5921
 
0.1%
277397 5761
 
0.1%
286432 5753
 
0.1%
Other values (25154) 6486401
98.6%
(Missing) 17562
 
0.3%
ValueCountFrequency (%)
267051 633
< 0.1%
267052 386
< 0.1%
267053 380
< 0.1%
267054 572
< 0.1%
267055 248
 
< 0.1%
267056 230
 
< 0.1%
267057 314
< 0.1%
267058 66
 
< 0.1%
267059 63
 
< 0.1%
267060 23
 
< 0.1%
ValueCountFrequency (%)
313609 17
 
< 0.1%
313608 2
 
< 0.1%
313607 2
 
< 0.1%
313606 15
 
< 0.1%
313605 10
 
< 0.1%
313604 27
 
< 0.1%
313603 794
< 0.1%
313602 4
 
< 0.1%
313601 1
 
< 0.1%
313600 86
 
< 0.1%

NOTA_POSTULA
Real number (ℝ)

Distinct525
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean759.48521
Minimum392
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:16.875724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum392
5-th percentile660
Q1715
median754
Q3801
95-th percentile877
Maximum1000
Range608
Interquartile range (IQR)86

Descriptive statistics

Standard deviation65.247155
Coefficient of variation (CV)0.085909711
Kurtosis0.11734681
Mean759.48521
Median Absolute Deviation (MAD)43
Skewness0.34512059
Sum4.9810032 × 109
Variance4257.1912
MonotonicityNot monotonic
2023-03-10T02:46:16.931200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
742 43303
 
0.7%
750 42987
 
0.7%
733 42878
 
0.7%
747 42856
 
0.7%
741 42742
 
0.6%
735 42585
 
0.6%
753 42531
 
0.6%
743 42475
 
0.6%
746 42445
 
0.6%
745 42396
 
0.6%
Other values (515) 6131196
93.2%
ValueCountFrequency (%)
392 2
 
< 0.1%
400 5
< 0.1%
433 1
 
< 0.1%
437 1
 
< 0.1%
439 1
 
< 0.1%
448 1
 
< 0.1%
453 2
 
< 0.1%
462 1
 
< 0.1%
464 3
 
< 0.1%
466 8
< 0.1%
ValueCountFrequency (%)
1000 215
< 0.1%
999 35
 
< 0.1%
998 37
 
< 0.1%
997 39
 
< 0.1%
996 70
 
< 0.1%
995 79
 
< 0.1%
994 67
 
< 0.1%
993 114
< 0.1%
992 107
< 0.1%
991 173
< 0.1%

PRD_ID_NUM_POSTULACION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean479.18149
Minimum446
Maximum1092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:16.977094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum446
5-th percentile446
Q1446
median446
Q3492
95-th percentile494
Maximum1092
Range646
Interquartile range (IQR)46

Descriptive statistics

Standard deviation89.484575
Coefficient of variation (CV)0.18674464
Kurtosis38.53175
Mean479.18149
Median Absolute Deviation (MAD)0
Skewness6.1183298
Sum3.1510764 × 109
Variance8007.4891
MonotonicityNot monotonic
2023-03-10T02:46:17.013539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
446 3597362
54.7%
492 1802730
27.4%
494 1037891
 
15.8%
1092 120411
 
1.8%
908 14819
 
0.2%
808 2058
 
< 0.1%
561 685
 
< 0.1%
ValueCountFrequency (%)
446 3597362
54.7%
492 1802730
27.4%
494 1037891
 
15.8%
561 685
 
< 0.1%
808 2058
 
< 0.1%
908 14819
 
0.2%
1092 120411
 
1.8%
ValueCountFrequency (%)
1092 120411
 
1.8%
908 14819
 
0.2%
808 2058
 
< 0.1%
561 685
 
< 0.1%
494 1037891
 
15.8%
492 1802730
27.4%
446 3597362
54.7%

POS_PRIORIDAD
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
1
1573514 
2
1449702 
3
1344376 
4
1177104 
5
1031260 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6575956
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row3
4th row1
5th row4

Common Values

ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

Length

2023-03-10T02:46:17.059599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:17.112348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

Most occurring characters

ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6575956
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 6575956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6575956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1573514
23.9%
2 1449702
22.0%
3 1344376
20.4%
4 1177104
17.9%
5 1031260
15.7%

POS_ESTADO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
1.0
6558214 
0.0
 
180

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19675182
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6558214
99.7%
0.0 180
 
< 0.1%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:17.159340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:17.208212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6558214
> 99.9%
0.0 180
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 6558574
33.3%
. 6558394
33.3%
1 6558214
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13116788
66.7%
Other Punctuation 6558394
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6558574
50.0%
1 6558214
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6558394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19675182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6558574
33.3%
. 6558394
33.3%
1 6558214
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19675182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6558574
33.3%
. 6558394
33.3%
1 6558214
33.3%

IES_ID
Real number (ℝ)

Distinct250
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.83078
Minimum22
Maximum1054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:17.254415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23
Q148
median59
Q385
95-th percentile513
Maximum1054
Range1032
Interquartile range (IQR)37

Descriptive statistics

Standard deviation177.00474
Coefficient of variation (CV)1.6116133
Kurtosis13.167232
Mean109.83078
Median Absolute Deviation (MAD)13
Skewness3.6550662
Sum7.2224235 × 108
Variance31330.678
MonotonicityNot monotonic
2023-03-10T02:46:17.315138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 1274395
19.4%
46 858900
13.1%
59 581336
 
8.8%
86 421493
 
6.4%
22 313590
 
4.8%
72 253365
 
3.9%
48 240948
 
3.7%
102 218339
 
3.3%
88 206074
 
3.1%
85 188422
 
2.9%
Other values (240) 2019094
30.7%
ValueCountFrequency (%)
22 313590
4.8%
23 29410
 
0.4%
29 113738
 
1.7%
30 17047
 
0.3%
31 134715
2.0%
32 44250
 
0.7%
38 6610
 
0.1%
39 53468
 
0.8%
43 347
 
< 0.1%
44 5966
 
0.1%
ValueCountFrequency (%)
1054 132
 
< 0.1%
1053 117
 
< 0.1%
1051 527
 
< 0.1%
1050 129
 
< 0.1%
1049 13
 
< 0.1%
1047 681
 
< 0.1%
1046 6450
 
0.1%
1045 411
 
< 0.1%
1040 10912
0.2%
1034 17938
0.3%
Distinct245
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
UNIVERSIDAD DE GUAYAQUIL
1274395 
UNIVERSIDAD CENTRAL DEL ECUADOR
858889 
UNIVERSIDAD ESTATAL DE MILAGRO
578566 
UNIVERSIDAD TECNICA DE MANABI
421269 
UNIVERSIDAD DE LAS FUERZAS ARMADAS (ESPE)
312102 
Other values (240)
3113173 

Length

Max length82
Median length76
Mean length31.835111
Min length15

Characters and Unicode

Total characters208787201
Distinct characters44
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNIVERSIDAD CENTRAL DEL ECUADOR
2nd rowUNIVERSIDAD ESTATAL DE MILAGRO
3rd rowUNIVERSIDAD NACIONAL DE LOJA
4th rowUNIVERSIDAD NACIONAL DE LOJA
5th rowUNIVERSIDAD NACIONAL DE LOJA

Common Values

ValueCountFrequency (%)
UNIVERSIDAD DE GUAYAQUIL 1274395
19.4%
UNIVERSIDAD CENTRAL DEL ECUADOR 858889
13.1%
UNIVERSIDAD ESTATAL DE MILAGRO 578566
 
8.8%
UNIVERSIDAD TECNICA DE MANABI 421269
 
6.4%
UNIVERSIDAD DE LAS FUERZAS ARMADAS (ESPE) 312102
 
4.7%
UNIVERSIDAD NACIONAL DE LOJA 253365
 
3.9%
UNIVERSIDAD DE CUENCA 240948
 
3.7%
UNIVERSIDAD LAICA ELOY ALFARO DE MANABI 218151
 
3.3%
UNIVERSIDAD TECNICA DEL NORTE 206074
 
3.1%
UNIVERSIDAD TECNICA DE MACHALA 188422
 
2.9%
Other values (235) 2006213
30.5%

Length

2023-03-10T02:46:17.498013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
universidad 5714024
21.0%
de 4739258
17.4%
tecnica 1419808
 
5.2%
guayaquil 1338198
 
4.9%
del 1289113
 
4.7%
estatal 966292
 
3.5%
ecuador 930261
 
3.4%
central 888299
 
3.3%
superior 730619
 
2.7%
manabi 726107
 
2.7%
Other values (373) 8499184
31.2%

Most occurring characters

ValueCountFrequency (%)
A 23937039
11.5%
E 21304068
10.2%
I 20733349
9.9%
20683724
9.9%
D 18987441
9.1%
N 12656324
 
6.1%
U 12199504
 
5.8%
R 12037258
 
5.8%
S 10587440
 
5.1%
L 9122560
 
4.4%
Other values (34) 46538494
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 187414670
89.8%
Space Separator 20683724
 
9.9%
Open Punctuation 315219
 
0.2%
Close Punctuation 315219
 
0.2%
Modifier Symbol 22951
 
< 0.1%
Decimal Number 14916
 
< 0.1%
Dash Punctuation 7254
 
< 0.1%
Lowercase Letter 7011
 
< 0.1%
Other Punctuation 6237
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 23937039
12.8%
E 21304068
11.4%
I 20733349
11.1%
D 18987441
10.1%
N 12656324
 
6.8%
U 12199504
 
6.5%
R 12037258
 
6.4%
S 10587440
 
5.6%
L 9122560
 
4.9%
C 9107142
 
4.9%
Other values (22) 36742545
19.6%
Decimal Number
ValueCountFrequency (%)
1 7350
49.3%
7 7350
49.3%
0 162
 
1.1%
2 54
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 5870
94.1%
' 367
 
5.9%
Space Separator
ValueCountFrequency (%)
20683724
100.0%
Open Punctuation
ValueCountFrequency (%)
( 315219
100.0%
Close Punctuation
ValueCountFrequency (%)
) 315219
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 22951
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7254
100.0%
Lowercase Letter
ValueCountFrequency (%)
ü 7011
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187421681
89.8%
Common 21365520
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 23937039
12.8%
E 21304068
11.4%
I 20733349
11.1%
D 18987441
10.1%
N 12656324
 
6.8%
U 12199504
 
6.5%
R 12037258
 
6.4%
S 10587440
 
5.6%
L 9122560
 
4.9%
C 9107142
 
4.9%
Other values (23) 36749556
19.6%
Common
ValueCountFrequency (%)
20683724
96.8%
( 315219
 
1.5%
) 315219
 
1.5%
´ 22951
 
0.1%
1 7350
 
< 0.1%
7 7350
 
< 0.1%
- 7254
 
< 0.1%
. 5870
 
< 0.1%
' 367
 
< 0.1%
0 162
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 208003981
99.6%
None 783220
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 23937039
11.5%
E 21304068
10.2%
I 20733349
10.0%
20683724
9.9%
D 18987441
9.1%
N 12656324
 
6.1%
U 12199504
 
5.9%
R 12037258
 
5.8%
S 10587440
 
5.1%
L 9122560
 
4.4%
Other values (26) 45755274
22.0%
None
ValueCountFrequency (%)
Ó 603049
77.0%
Í 89625
 
11.4%
É 39043
 
5.0%
´ 22951
 
2.9%
Ñ 14937
 
1.9%
ü 7011
 
0.9%
Á 4048
 
0.5%
Ú 2556
 
0.3%

IES_TIPO_IES
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
U
6021393 
I
 
537001

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6558394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 6021393
91.6%
I 537001
 
8.2%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:17.554895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:17.601503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
u 6021393
91.8%
i 537001
 
8.2%

Most occurring characters

ValueCountFrequency (%)
U 6021393
91.8%
I 537001
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6558394
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 6021393
91.8%
I 537001
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 6558394
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 6021393
91.8%
I 537001
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6558394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 6021393
91.8%
I 537001
 
8.2%

IES_TIPO_FINANCIAMIENTO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
PÚBLICA
6421374 
COFINANCIADA
 
68817
AUTOFINANCIADA
 
68203

Length

Max length14
Median length7
Mean length7.1252602
Min length7

Characters and Unicode

Total characters46730264
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPÚBLICA
2nd rowPÚBLICA
3rd rowPÚBLICA
4th rowPÚBLICA
5th rowPÚBLICA

Common Values

ValueCountFrequency (%)
PÚBLICA 6421374
97.6%
COFINANCIADA 68817
 
1.0%
AUTOFINANCIADA 68203
 
1.0%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:17.644751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:17.696843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pública 6421374
97.9%
cofinanciada 68817
 
1.0%
autofinanciada 68203
 
1.0%

Most occurring characters

ValueCountFrequency (%)
A 6900637
14.8%
I 6695414
14.3%
C 6627211
14.2%
P 6421374
13.7%
Ú 6421374
13.7%
B 6421374
13.7%
L 6421374
13.7%
N 274040
 
0.6%
O 137020
 
0.3%
F 137020
 
0.3%
Other values (3) 273426
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 46730264
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6900637
14.8%
I 6695414
14.3%
C 6627211
14.2%
P 6421374
13.7%
Ú 6421374
13.7%
B 6421374
13.7%
L 6421374
13.7%
N 274040
 
0.6%
O 137020
 
0.3%
F 137020
 
0.3%
Other values (3) 273426
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 46730264
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6900637
14.8%
I 6695414
14.3%
C 6627211
14.2%
P 6421374
13.7%
Ú 6421374
13.7%
B 6421374
13.7%
L 6421374
13.7%
N 274040
 
0.6%
O 137020
 
0.3%
F 137020
 
0.3%
Other values (3) 273426
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40308890
86.3%
None 6421374
 
13.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6900637
17.1%
I 6695414
16.6%
C 6627211
16.4%
P 6421374
15.9%
B 6421374
15.9%
L 6421374
15.9%
N 274040
 
0.7%
O 137020
 
0.3%
F 137020
 
0.3%
D 137020
 
0.3%
Other values (2) 136406
 
0.3%
None
ValueCountFrequency (%)
Ú 6421374
100.0%

OFA_ID
Real number (ℝ)

Distinct11597
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152032.9
Minimum93025
Maximum183438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:17.747884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum93025
5-th percentile96115
Q1144010
median158819
Q3171929
95-th percentile180709
Maximum183438
Range90413
Interquartile range (IQR)27919

Descriptive statistics

Standard deviation26147.786
Coefficient of variation (CV)0.17198768
Kurtosis0.14055579
Mean152032.9
Median Absolute Deviation (MAD)13614
Skewness-1.1080245
Sum9.9976165 × 1011
Variance6.837067 × 108
MonotonicityNot monotonic
2023-03-10T02:46:17.803991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176762 18148
 
0.3%
180856 17440
 
0.3%
167181 15361
 
0.2%
148309 14760
 
0.2%
179253 14689
 
0.2%
180490 14551
 
0.2%
182232 14521
 
0.2%
174129 14287
 
0.2%
167183 12879
 
0.2%
172328 12527
 
0.2%
Other values (11587) 6426793
97.7%
ValueCountFrequency (%)
93025 1455
 
< 0.1%
93028 5748
0.1%
93029 752
 
< 0.1%
93034 442
 
< 0.1%
93037 2103
 
< 0.1%
93040 1303
 
< 0.1%
93041 2277
 
< 0.1%
93047 1424
 
< 0.1%
93051 895
 
< 0.1%
93054 3099
< 0.1%
ValueCountFrequency (%)
183438 435
 
< 0.1%
183436 38
 
< 0.1%
183433 369
 
< 0.1%
183432 333
 
< 0.1%
183429 183
 
< 0.1%
183425 530
 
< 0.1%
183424 535
 
< 0.1%
183408 548
 
< 0.1%
183402 158
 
< 0.1%
183395 2718
< 0.1%

IES_ESTADO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
A
6557907 
I
 
487

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6558394
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 6557907
99.7%
I 487
 
< 0.1%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:17.854613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:17.899980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
a 6557907
> 99.9%
i 487
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 6557907
> 99.9%
I 487
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6558394
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6557907
> 99.9%
I 487
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6558394
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6557907
> 99.9%
I 487
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6558394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6557907
> 99.9%
I 487
 
< 0.1%

APC_ID
Real number (ℝ)

Distinct8720
Distinct (%)0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean40587.797
Minimum27861
Maximum48518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:17.946650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum27861
5-th percentile28648
Q137848
median41681
Q345335
95-th percentile47747
Maximum48518
Range20657
Interquartile range (IQR)7487

Descriptive statistics

Standard deviation5870.914
Coefficient of variation (CV)0.14464727
Kurtosis-0.32792615
Mean40587.797
Median Absolute Deviation (MAD)3785
Skewness-0.8157006
Sum2.6619076 × 1011
Variance34467631
MonotonicityNot monotonic
2023-03-10T02:46:18.000975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38987 21699
 
0.3%
46270 19960
 
0.3%
43645 19129
 
0.3%
45466 19005
 
0.3%
45103 18801
 
0.3%
46269 18400
 
0.3%
36736 18239
 
0.3%
47778 18148
 
0.3%
43996 16976
 
0.3%
38524 16519
 
0.3%
Other values (8710) 6371518
96.9%
(Missing) 17562
 
0.3%
ValueCountFrequency (%)
27861 2258
< 0.1%
27862 1161
< 0.1%
27863 700
 
< 0.1%
27864 1495
< 0.1%
27865 1292
< 0.1%
27866 1624
< 0.1%
27867 2277
< 0.1%
27868 2319
< 0.1%
27871 1233
< 0.1%
27872 480
 
< 0.1%
ValueCountFrequency (%)
48518 243
 
< 0.1%
48517 1078
 
< 0.1%
48516 81
 
< 0.1%
48512 435
 
< 0.1%
48511 2718
< 0.1%
48510 12
 
< 0.1%
48509 179
 
< 0.1%
48508 571
 
< 0.1%
48507 216
 
< 0.1%
48506 493
 
< 0.1%

CCP_ID
Real number (ℝ)

Distinct11597
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25891.568
Minimum19105
Maximum32288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:18.057275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19105
5-th percentile19979
Q122417
median26042
Q329095
95-th percentile31858
Maximum32288
Range13183
Interquartile range (IQR)6678

Descriptive statistics

Standard deviation3762.3523
Coefficient of variation (CV)0.14531187
Kurtosis-1.1894783
Mean25891.568
Median Absolute Deviation (MAD)3422
Skewness-0.033997259
Sum1.7026182 × 1011
Variance14155295
MonotonicityNot monotonic
2023-03-10T02:46:18.117663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30197 18148
 
0.3%
30216 17440
 
0.3%
29091 15361
 
0.2%
22365 14760
 
0.2%
29284 14689
 
0.2%
32071 14551
 
0.2%
30201 14521
 
0.2%
30220 14287
 
0.2%
29076 12879
 
0.2%
32087 12527
 
0.2%
Other values (11587) 6426793
97.7%
ValueCountFrequency (%)
19105 24
< 0.1%
19109 57
< 0.1%
19113 24
< 0.1%
19117 21
 
< 0.1%
19121 24
< 0.1%
19125 24
< 0.1%
19129 20
 
< 0.1%
19133 29
< 0.1%
19134 6
 
< 0.1%
19135 5
 
< 0.1%
ValueCountFrequency (%)
32288 381
< 0.1%
32268 69
 
< 0.1%
32256 548
< 0.1%
32255 530
< 0.1%
32254 69
 
< 0.1%
32253 57
 
< 0.1%
32252 85
 
< 0.1%
32251 131
 
< 0.1%
32250 201
 
< 0.1%
32249 233
< 0.1%

CAR_ID
Real number (ℝ)

Distinct420
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5310.1894
Minimum4447
Maximum7603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:18.177713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4447
5-th percentile4462
Q14641
median5043
Q35457
95-th percentile7203
Maximum7603
Range3156
Interquartile range (IQR)816

Descriptive statistics

Standard deviation872.93091
Coefficient of variation (CV)0.1643879
Kurtosis0.54862205
Mean5310.1894
Median Absolute Deviation (MAD)414
Skewness1.3053596
Sum3.4919572 × 1010
Variance762008.38
MonotonicityNot monotonic
2023-03-10T02:46:18.232904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5455 316362
 
4.8%
4781 300470
 
4.6%
5476 288861
 
4.4%
4641 278625
 
4.2%
4473 274270
 
4.2%
4478 256306
 
3.9%
5334 212909
 
3.2%
5457 210782
 
3.2%
4458 200350
 
3.0%
4462 196591
 
3.0%
Other values (410) 4040430
61.4%
ValueCountFrequency (%)
4447 593
 
< 0.1%
4458 200350
3.0%
4459 55800
 
0.8%
4460 8359
 
0.1%
4461 41599
 
0.6%
4462 196591
3.0%
4470 127
 
< 0.1%
4473 274270
4.2%
4474 123605
1.9%
4478 256306
3.9%
ValueCountFrequency (%)
7603 346
< 0.1%
7602 38
 
< 0.1%
7601 74
 
< 0.1%
7600 243
< 0.1%
7598 2
 
< 0.1%
7597 19
 
< 0.1%
7596 7
 
< 0.1%
7595 18
 
< 0.1%
7593 422
< 0.1%
7587 7
 
< 0.1%
Distinct410
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
ADMINISTRACION DE EMPRESAS
 
316857
DERECHO
 
303256
PSICOLOGIA
 
288861
ENFERMERIA
 
278776
EDUCACION INICIAL
 
273737
Other values (405)
5096907 

Length

Max length99
Median length78
Mean length18.970351
Min length4

Characters and Unicode

Total characters124415036
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowADMINISTRACION DE EMPRESAS
2nd rowCOMUNICACION
3rd rowDERECHO
4th rowCOMUNICACION
5th rowEDUCACION INICIAL

Common Values

ValueCountFrequency (%)
ADMINISTRACION DE EMPRESAS 316857
 
4.8%
DERECHO 303256
 
4.6%
PSICOLOGIA 288861
 
4.4%
ENFERMERIA 278776
 
4.2%
EDUCACION INICIAL 273737
 
4.2%
EDUCACION BASICA 256102
 
3.9%
ECONOMIA 212543
 
3.2%
TURISMO 209349
 
3.2%
CONTABILIDAD Y AUDITORIA 200125
 
3.0%
MEDICINA 196591
 
3.0%
Other values (400) 4022197
61.2%

Length

2023-03-10T02:46:18.298524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 1064811
 
7.0%
y 796864
 
5.3%
educacion 557321
 
3.7%
tecnologia 539182
 
3.6%
superior 538249
 
3.6%
en 513685
 
3.4%
ingenieria 446759
 
2.9%
pedagogia 391950
 
2.6%
la 387082
 
2.6%
administracion 380075
 
2.5%
Other values (412) 9531334
62.9%

Most occurring characters

ValueCountFrequency (%)
I 16422565
13.2%
A 14013504
11.3%
E 12035749
9.7%
O 10935687
8.8%
C 8619180
 
6.9%
8589381
 
6.9%
N 8559911
 
6.9%
R 7318067
 
5.9%
S 5367373
 
4.3%
D 5301485
 
4.3%
Other values (26) 27252134
21.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 115820336
93.1%
Space Separator 8589381
 
6.9%
Other Punctuation 5139
 
< 0.1%
Dash Punctuation 180
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 16422565
14.2%
A 14013504
12.1%
E 12035749
10.4%
O 10935687
9.4%
C 8619180
 
7.4%
N 8559911
 
7.4%
R 7318067
 
6.3%
S 5367373
 
4.6%
D 5301485
 
4.6%
T 5261412
 
4.5%
Other values (23) 21985403
19.0%
Space Separator
ValueCountFrequency (%)
8589381
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5139
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115820336
93.1%
Common 8594700
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 16422565
14.2%
A 14013504
12.1%
E 12035749
10.4%
O 10935687
9.4%
C 8619180
 
7.4%
N 8559911
 
7.4%
R 7318067
 
6.3%
S 5367373
 
4.6%
D 5301485
 
4.6%
T 5261412
 
4.5%
Other values (23) 21985403
19.0%
Common
ValueCountFrequency (%)
8589381
99.9%
, 5139
 
0.1%
- 180
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124258350
99.9%
None 156686
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 16422565
13.2%
A 14013504
11.3%
E 12035749
9.7%
O 10935687
8.8%
C 8619180
 
6.9%
8589381
 
6.9%
N 8559911
 
6.9%
R 7318067
 
5.9%
S 5367373
 
4.3%
D 5301485
 
4.3%
Other values (19) 27095448
21.8%
None
ValueCountFrequency (%)
Ñ 145058
92.6%
Ó 7694
 
4.9%
Í 1953
 
1.2%
Ü 1679
 
1.1%
É 138
 
0.1%
Ú 129
 
0.1%
Á 35
 
< 0.1%

MODALIDAD_ID
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean66.486144
Minimum8
Maximum940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:18.344572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19
median9
Q39
95-th percentile441
Maximum940
Range932
Interquartile range (IQR)0

Descriptive statistics

Standard deviation152.52656
Coefficient of variation (CV)2.2941104
Kurtosis5.5637358
Mean66.486144
Median Absolute Deviation (MAD)0
Skewness2.5037682
Sum4.3604233 × 108
Variance23264.35
MonotonicityNot monotonic
2023-03-10T02:46:18.379910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9 5491921
83.5%
441 745794
 
11.3%
8 143909
 
2.2%
10 78000
 
1.2%
442 74408
 
1.1%
940 24362
 
0.4%
(Missing) 17562
 
0.3%
ValueCountFrequency (%)
8 143909
 
2.2%
9 5491921
83.5%
10 78000
 
1.2%
441 745794
 
11.3%
442 74408
 
1.1%
940 24362
 
0.4%
ValueCountFrequency (%)
940 24362
 
0.4%
442 74408
 
1.1%
441 745794
 
11.3%
10 78000
 
1.2%
9 5491921
83.5%
8 143909
 
2.2%

MODALIDAD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
PRESENCIAL
5504828 
EN LINEA
749282 
DISTANCIA
 
144133
SEMI-PRESENCIAL
 
78453
DUAL
 
74663

Length

Max length15
Median length10
Mean length9.7305029
Min length4

Characters and Unicode

Total characters63987359
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDISTANCIA
2nd rowEN LINEA
3rd rowDISTANCIA
4th rowDISTANCIA
5th rowDISTANCIA

Common Values

ValueCountFrequency (%)
PRESENCIAL 5504828
83.7%
EN LINEA 749282
 
11.4%
DISTANCIA 144133
 
2.2%
SEMI-PRESENCIAL 78453
 
1.2%
DUAL 74663
 
1.1%
HIBRIDA 24597
 
0.4%

Length

2023-03-10T02:46:18.424295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:18.478163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
presencial 5504828
75.1%
en 749282
 
10.2%
linea 749282
 
10.2%
distancia 144133
 
2.0%
semi-presencial 78453
 
1.1%
dual 74663
 
1.0%
hibrida 24597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 12743579
19.9%
N 7225978
11.3%
I 6748476
10.5%
A 6720089
10.5%
L 6407226
10.0%
S 5805867
9.1%
C 5727414
9.0%
R 5607878
8.8%
P 5583281
8.7%
749282
 
1.2%
Other values (7) 668289
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 63159624
98.7%
Space Separator 749282
 
1.2%
Dash Punctuation 78453
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 12743579
20.2%
N 7225978
11.4%
I 6748476
10.7%
A 6720089
10.6%
L 6407226
10.1%
S 5805867
9.2%
C 5727414
9.1%
R 5607878
8.9%
P 5583281
8.8%
D 243393
 
0.4%
Other values (5) 346443
 
0.5%
Space Separator
ValueCountFrequency (%)
749282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63159624
98.7%
Common 827735
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 12743579
20.2%
N 7225978
11.4%
I 6748476
10.7%
A 6720089
10.6%
L 6407226
10.1%
S 5805867
9.2%
C 5727414
9.1%
R 5607878
8.9%
P 5583281
8.8%
D 243393
 
0.4%
Other values (5) 346443
 
0.5%
Common
ValueCountFrequency (%)
749282
90.5%
- 78453
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63987359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 12743579
19.9%
N 7225978
11.3%
I 6748476
10.5%
A 6720089
10.5%
L 6407226
10.0%
S 5805867
9.1%
C 5727414
9.0%
R 5607878
8.8%
P 5583281
8.7%
749282
 
1.2%
Other values (7) 668289
 
1.0%

JORNADA_ID
Categorical

Distinct5
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
1.0
1897593 
2.0
1714874 
7.0
1285804 
3.0
992065 
5.0
668058 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19675182
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 1897593
28.9%
2.0 1714874
26.1%
7.0 1285804
19.6%
3.0 992065
15.1%
5.0 668058
 
10.2%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:18.525954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:18.577497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1897593
28.9%
2.0 1714874
26.1%
7.0 1285804
19.6%
3.0 992065
15.1%
5.0 668058
 
10.2%

Most occurring characters

ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 1897593
 
9.6%
2 1714874
 
8.7%
7 1285804
 
6.5%
3 992065
 
5.0%
5 668058
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13116788
66.7%
Other Punctuation 6558394
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6558394
50.0%
1 1897593
 
14.5%
2 1714874
 
13.1%
7 1285804
 
9.8%
3 992065
 
7.6%
5 668058
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 6558394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19675182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 1897593
 
9.6%
2 1714874
 
8.7%
7 1285804
 
6.5%
3 992065
 
5.0%
5 668058
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19675182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 6558394
33.3%
0 6558394
33.3%
1 1897593
 
9.6%
2 1714874
 
8.7%
7 1285804
 
6.5%
3 992065
 
5.0%
5 668058
 
3.4%

JORNADA
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
INTENSIVA
1900676 
MATUTINA
1719108 
VESPERTINA
1288185 
NO APLICA JORNADA
996465 
NOCTURNA
671522 

Length

Max length17
Median length10
Mean length10.044605
Min length8

Characters and Unicode

Total characters66052879
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO APLICA JORNADA
2nd rowNO APLICA JORNADA
3rd rowNO APLICA JORNADA
4th rowNO APLICA JORNADA
5th rowNO APLICA JORNADA

Common Values

ValueCountFrequency (%)
INTENSIVA 1900676
28.9%
MATUTINA 1719108
26.1%
VESPERTINA 1288185
19.6%
NO APLICA JORNADA 996465
15.2%
NOCTURNA 671522
 
10.2%

Length

2023-03-10T02:46:18.623797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:18.675009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
intensiva 1900676
22.2%
matutina 1719108
20.1%
vespertina 1288185
15.0%
no 996465
11.6%
aplica 996465
11.6%
jornada 996465
11.6%
nocturna 671522
 
7.8%

Most occurring characters

ValueCountFrequency (%)
A 11284459
17.1%
N 10144619
15.4%
I 7805110
11.8%
T 7298599
11.0%
E 4477046
 
6.8%
S 3188861
 
4.8%
V 3188861
 
4.8%
R 2956172
 
4.5%
O 2664452
 
4.0%
U 2390630
 
3.6%
Other values (7) 10654070
16.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 64059949
97.0%
Space Separator 1992930
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 11284459
17.6%
N 10144619
15.8%
I 7805110
12.2%
T 7298599
11.4%
E 4477046
 
7.0%
S 3188861
 
5.0%
V 3188861
 
5.0%
R 2956172
 
4.6%
O 2664452
 
4.2%
U 2390630
 
3.7%
Other values (6) 8661140
13.5%
Space Separator
ValueCountFrequency (%)
1992930
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64059949
97.0%
Common 1992930
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 11284459
17.6%
N 10144619
15.8%
I 7805110
12.2%
T 7298599
11.4%
E 4477046
 
7.0%
S 3188861
 
5.0%
V 3188861
 
5.0%
R 2956172
 
4.6%
O 2664452
 
4.2%
U 2390630
 
3.7%
Other values (6) 8661140
13.5%
Common
ValueCountFrequency (%)
1992930
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66052879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 11284459
17.1%
N 10144619
15.4%
I 7805110
11.8%
T 7298599
11.0%
E 4477046
 
6.8%
S 3188861
 
4.8%
V 3188861
 
4.8%
R 2956172
 
4.5%
O 2664452
 
4.0%
U 2390630
 
3.6%
Other values (7) 10654070
16.1%

NIVEL
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
TERCER NIVEL
6002931 
TERCER NIVEL TECNOLÓGICO SUPERIOR
 
445771
TECNOLOGICO SUPERIOR
 
108628
TERCER NIVEL TÉCNICO SUPERIOR
 
15827
TECNICO SUPERIOR
 
2799

Length

Max length33
Median length12
Mean length13.598318
Min length12

Characters and Unicode

Total characters89421942
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTERCER NIVEL
2nd rowTERCER NIVEL
3rd rowTERCER NIVEL
4th rowTERCER NIVEL
5th rowTERCER NIVEL

Common Values

ValueCountFrequency (%)
TERCER NIVEL 6002931
91.3%
TERCER NIVEL TECNOLÓGICO SUPERIOR 445771
 
6.8%
TECNOLOGICO SUPERIOR 108628
 
1.7%
TERCER NIVEL TÉCNICO SUPERIOR 15827
 
0.2%
TECNICO SUPERIOR 2799
 
< 0.1%

Length

2023-03-10T02:46:18.724408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:18.774353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
tercer 6464529
45.9%
nivel 6464529
45.9%
superior 573025
 
4.1%
tecnológico 445771
 
3.2%
tecnologico 108628
 
0.8%
técnico 15827
 
0.1%
tecnico 2799
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 20523810
23.0%
R 14075108
15.7%
C 7610579
 
8.5%
I 7610579
 
8.5%
7499152
 
8.4%
T 7037554
 
7.9%
N 7037554
 
7.9%
L 7018928
 
7.8%
V 6464529
 
7.2%
O 1809077
 
2.0%
Other values (6) 2735072
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 81922790
91.6%
Space Separator 7499152
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 20523810
25.1%
R 14075108
17.2%
C 7610579
 
9.3%
I 7610579
 
9.3%
T 7037554
 
8.6%
N 7037554
 
8.6%
L 7018928
 
8.6%
V 6464529
 
7.9%
O 1809077
 
2.2%
S 573025
 
0.7%
Other values (5) 2162047
 
2.6%
Space Separator
ValueCountFrequency (%)
7499152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81922790
91.6%
Common 7499152
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 20523810
25.1%
R 14075108
17.2%
C 7610579
 
9.3%
I 7610579
 
9.3%
T 7037554
 
8.6%
N 7037554
 
8.6%
L 7018928
 
8.6%
V 6464529
 
7.9%
O 1809077
 
2.2%
S 573025
 
0.7%
Other values (5) 2162047
 
2.6%
Common
ValueCountFrequency (%)
7499152
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88960344
99.5%
None 461598
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 20523810
23.1%
R 14075108
15.8%
C 7610579
 
8.6%
I 7610579
 
8.6%
7499152
 
8.4%
T 7037554
 
7.9%
N 7037554
 
7.9%
L 7018928
 
7.9%
V 6464529
 
7.3%
O 1809077
 
2.0%
Other values (4) 2273474
 
2.6%
None
ValueCountFrequency (%)
Ó 445771
96.6%
É 15827
 
3.4%

AREA_ID
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.31852
Minimum2
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:18.816822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q113
median14
Q317
95-th percentile26
Maximum26
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2569194
Coefficient of variation (CV)0.34317411
Kurtosis0.030071772
Mean15.31852
Median Absolute Deviation (MAD)2
Skewness0.94709887
Sum1.0046489 × 108
Variance27.635201
MonotonicityNot monotonic
2023-03-10T02:46:18.860469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 1226193
18.6%
16 1053636
16.0%
14 993428
15.1%
26 988036
15.0%
9 950116
14.4%
18 345050
 
5.2%
17 336311
 
5.1%
10 332232
 
5.1%
11 176959
 
2.7%
12 101415
 
1.5%
Other values (6) 55018
 
0.8%
ValueCountFrequency (%)
2 84
 
< 0.1%
3 5020
 
0.1%
4 498
 
< 0.1%
7 5816
 
0.1%
9 950116
14.4%
10 332232
 
5.1%
11 176959
 
2.7%
12 101415
 
1.5%
13 1226193
18.6%
14 993428
15.1%
ValueCountFrequency (%)
26 988036
15.0%
24 43531
 
0.7%
22 69
 
< 0.1%
18 345050
 
5.2%
17 336311
 
5.1%
16 1053636
16.0%
14 993428
15.1%
13 1226193
18.6%
12 101415
 
1.5%
11 176959
 
2.7%

AREA_NOMBRE
Categorical

Distinct16
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
1226193 
SALUD Y BIENESTAR
1053636 
EDUCACION
993428 
INGENIERIA, INDUSTRIA Y CONSTRUCCION
988036 
ADMINISTRACION
950116 
Other values (11)
1346985 

Length

Max length53
Median length48
Mean length28.416752
Min length8

Characters and Unicode

Total characters186368255
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowADMINISTRACION
2nd rowCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
3rd rowCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
4th rowCIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO
5th rowEDUCACION

Common Values

ValueCountFrequency (%)
CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO 1226193
18.6%
SALUD Y BIENESTAR 1053636
16.0%
EDUCACION 993428
15.1%
INGENIERIA, INDUSTRIA Y CONSTRUCCION 988036
15.0%
ADMINISTRACION 950116
14.4%
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC) 345050
 
5.2%
SERVICIOS 336311
 
5.1%
AGRICULTURA, SILVICULTURA, PESCA Y VETERINARIA 332232
 
5.1%
ARTES Y HUMANIDADES 176959
 
2.7%
CIENCIAS NATURALES, MATEMATICAS Y ESTADISTICA 101415
 
1.5%
Other values (6) 55018
 
0.8%

Length

2023-03-10T02:46:18.908050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 4277972
19.0%
informacion 1571243
 
7.0%
ciencias 1376726
 
6.1%
sociales 1237098
 
5.5%
derecho 1231213
 
5.5%
periodismo 1226262
 
5.5%
salud 1059452
 
4.7%
bienestar 1053636
 
4.7%
educacion 998448
 
4.4%
ingenieria 988036
 
4.4%
Other values (23) 7461625
33.2%

Most occurring characters

ValueCountFrequency (%)
I 24715330
13.3%
C 16068200
8.6%
A 16053808
8.6%
15923317
8.5%
N 15101217
 
8.1%
E 14207240
 
7.6%
O 13715373
 
7.4%
S 12744098
 
6.8%
R 11326943
 
6.1%
D 7297609
 
3.9%
Other values (17) 39215120
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 165499917
88.8%
Space Separator 15923317
 
8.5%
Other Punctuation 4254921
 
2.3%
Open Punctuation 345050
 
0.2%
Close Punctuation 345050
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 24715330
14.9%
C 16068200
9.7%
A 16053808
9.7%
N 15101217
9.1%
E 14207240
8.6%
O 13715373
8.3%
S 12744098
7.7%
R 11326943
6.8%
D 7297609
 
4.4%
T 6568393
 
4.0%
Other values (13) 27701706
16.7%
Space Separator
ValueCountFrequency (%)
15923317
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4254921
100.0%
Open Punctuation
ValueCountFrequency (%)
( 345050
100.0%
Close Punctuation
ValueCountFrequency (%)
) 345050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 165499917
88.8%
Common 20868338
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 24715330
14.9%
C 16068200
9.7%
A 16053808
9.7%
N 15101217
9.1%
E 14207240
8.6%
O 13715373
8.3%
S 12744098
7.7%
R 11326943
6.8%
D 7297609
 
4.4%
T 6568393
 
4.0%
Other values (13) 27701706
16.7%
Common
ValueCountFrequency (%)
15923317
76.3%
, 4254921
 
20.4%
( 345050
 
1.7%
) 345050
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186281124
> 99.9%
None 87131
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 24715330
13.3%
C 16068200
8.6%
A 16053808
8.6%
15923317
8.5%
N 15101217
 
8.1%
E 14207240
 
7.6%
O 13715373
 
7.4%
S 12744098
 
6.8%
R 11326943
 
6.1%
D 7297609
 
3.9%
Other values (14) 39127989
21.0%
None
ValueCountFrequency (%)
Á 43531
50.0%
Í 43531
50.0%
Ó 69
 
0.1%

SUBAREA_ID
Real number (ℝ)

Distinct35
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean411.86731
Minimum5
Maximum519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:18.956204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q1500
median509
Q3512
95-th percentile517
Maximum519
Range514
Interquartile range (IQR)12

Descriptive statistics

Standard deviation187.5657
Coefficient of variation (CV)0.45540322
Kurtosis0.04541427
Mean411.86731
Median Absolute Deviation (MAD)5
Skewness-1.4217445
Sum2.7011881 × 109
Variance35180.891
MonotonicityNot monotonic
2023-03-10T02:46:19.007998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
512 1030591
15.7%
511 993428
15.1%
500 950116
14.4%
509 727852
11.1%
63 508168
7.7%
517 345050
 
5.2%
516 307699
 
4.7%
510 300523
 
4.6%
64 253868
 
3.9%
65 226000
 
3.4%
Other values (25) 915099
13.9%
ValueCountFrequency (%)
5 123689
1.9%
15 197818
3.0%
20 23045
 
0.4%
28 84
 
< 0.1%
30 274
 
< 0.1%
32 1937
 
< 0.1%
33 2809
 
< 0.1%
37 498
 
< 0.1%
43 5661
 
0.1%
44 155
 
< 0.1%
ValueCountFrequency (%)
519 49
 
< 0.1%
517 345050
 
5.2%
516 307699
 
4.7%
515 18538
 
0.3%
514 427
 
< 0.1%
513 9647
 
0.1%
512 1030591
15.7%
511 993428
15.1%
510 300523
 
4.6%
509 727852
11.1%

SUBAREA_NOMBRE
Categorical

Distinct30
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
SALUD
1030591 
EDUCACION
993428 
EDUCACION COMERCIAL Y ADMINISTRACION
952053 
CIENCIAS SOCIALES Y DEL COMPORTAMIENTO
728126 
INGENIERIA Y PROFESIONES AFINES
508168 
Other values (25)
2346028 

Length

Max length53
Median length31
Mean length21.88525
Min length5

Characters and Unicode

Total characters143532095
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEDUCACION COMERCIAL Y ADMINISTRACION
2nd rowPERIODISMO E INFORMACION
3rd rowDERECHO
4th rowPERIODISMO E INFORMACION
5th rowEDUCACION

Common Values

ValueCountFrequency (%)
SALUD 1030591
15.7%
EDUCACION 993428
15.1%
EDUCACION COMERCIAL Y ADMINISTRACION 952053
14.5%
CIENCIAS SOCIALES Y DEL COMPORTAMIENTO 728126
11.1%
INGENIERIA Y PROFESIONES AFINES 508168
7.7%
TECNOLOGIAS DE LA INFORMACION Y LA COMUNICACION (TIC) 345050
 
5.2%
SERVICIOS PERSONALES 307699
 
4.7%
DERECHO 303381
 
4.6%
INDUSTRIA Y PRODUCCION 253868
 
3.9%
ARQUITECTURA Y CONSTRUCCION 226000
 
3.4%
Other values (20) 910030
13.8%

Length

2023-03-10T02:46:19.061284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y 3108451
17.0%
educacion 1945481
 
10.6%
salud 1030591
 
5.6%
comercial 952053
 
5.2%
administracion 952053
 
5.2%
ciencias 829541
 
4.5%
sociales 728281
 
4.0%
del 728126
 
4.0%
comportamiento 728126
 
4.0%
la 690100
 
3.8%
Other values (37) 6602108
36.1%

Most occurring characters

ValueCountFrequency (%)
I 17050055
11.9%
C 14573358
10.2%
A 13252455
9.2%
O 12736468
8.9%
E 12040850
8.4%
11736517
8.2%
N 11093389
 
7.7%
S 8643012
 
6.0%
R 7385718
 
5.1%
D 6090923
 
4.2%
Other values (15) 28929350
20.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 131105478
91.3%
Space Separator 11736517
 
8.2%
Open Punctuation 345050
 
0.2%
Close Punctuation 345050
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 17050055
13.0%
C 14573358
11.1%
A 13252455
10.1%
O 12736468
9.7%
E 12040850
9.2%
N 11093389
8.5%
S 8643012
 
6.6%
R 7385718
 
5.6%
D 6090923
 
4.6%
L 5061981
 
3.9%
Other values (12) 23177269
17.7%
Space Separator
ValueCountFrequency (%)
11736517
100.0%
Open Punctuation
ValueCountFrequency (%)
( 345050
100.0%
Close Punctuation
ValueCountFrequency (%)
) 345050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131105478
91.3%
Common 12426617
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 17050055
13.0%
C 14573358
11.1%
A 13252455
10.1%
O 12736468
9.7%
E 12040850
9.2%
N 11093389
8.5%
S 8643012
 
6.6%
R 7385718
 
5.6%
D 6090923
 
4.6%
L 5061981
 
3.9%
Other values (12) 23177269
17.7%
Common
ValueCountFrequency (%)
11736517
94.4%
( 345050
 
2.8%
) 345050
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143532075
> 99.9%
None 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 17050055
11.9%
C 14573358
10.2%
A 13252455
9.2%
O 12736468
8.9%
E 12040850
8.4%
11736517
8.2%
N 11093389
 
7.7%
S 8643012
 
6.0%
R 7385718
 
5.1%
D 6090923
 
4.2%
Other values (14) 28929330
20.2%
None
ValueCountFrequency (%)
Ó 20
100.0%

PROVINCIA
Categorical

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
GUAYAS
2160962 
PICHINCHA
1430371 
MANABI
750561 
LOS RIOS
282221 
LOJA
268672 
Other values (19)
1683169 

Length

Max length30
Median length16
Mean length7.3250078
Min length4

Characters and Unicode

Total characters48168929
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPICHINCHA
2nd rowGUAYAS
3rd rowLOJA
4th rowLOJA
5th rowLOJA

Common Values

ValueCountFrequency (%)
GUAYAS 2160962
32.9%
PICHINCHA 1430371
21.8%
MANABI 750561
 
11.4%
LOS RIOS 282221
 
4.3%
LOJA 268672
 
4.1%
AZUAY 267075
 
4.1%
CHIMBORAZO 251570
 
3.8%
IMBABURA 233982
 
3.6%
EL ORO 215532
 
3.3%
TUNGURAHUA 210694
 
3.2%
Other values (14) 504316
 
7.7%

Length

2023-03-10T02:46:19.218258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guayas 2160962
29.3%
pichincha 1430371
19.4%
manabi 750561
 
10.2%
los 327248
 
4.4%
rios 282221
 
3.8%
loja 268672
 
3.6%
azuay 267075
 
3.6%
chimborazo 251570
 
3.4%
imbabura 233982
 
3.2%
el 215532
 
2.9%
Other values (22) 1191407
16.1%

Most occurring characters

ValueCountFrequency (%)
A 10163079
21.1%
I 4690319
9.7%
H 3396449
 
7.1%
C 3363484
 
7.0%
U 3305229
 
6.9%
S 3198663
 
6.6%
N 2746745
 
5.7%
Y 2428037
 
5.0%
G 2422501
 
5.0%
O 2299047
 
4.8%
Other values (14) 10155376
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47365284
98.3%
Space Separator 803645
 
1.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 10163079
21.5%
I 4690319
9.9%
H 3396449
 
7.2%
C 3363484
 
7.1%
U 3305229
 
7.0%
S 3198663
 
6.8%
N 2746745
 
5.8%
Y 2428037
 
5.1%
G 2422501
 
5.1%
O 2299047
 
4.9%
Other values (13) 9351731
19.7%
Space Separator
ValueCountFrequency (%)
803645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47365284
98.3%
Common 803645
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 10163079
21.5%
I 4690319
9.9%
H 3396449
 
7.2%
C 3363484
 
7.1%
U 3305229
 
7.0%
S 3198663
 
6.8%
N 2746745
 
5.8%
Y 2428037
 
5.1%
G 2422501
 
5.1%
O 2299047
 
4.9%
Other values (13) 9351731
19.7%
Common
ValueCountFrequency (%)
803645
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48148768
> 99.9%
None 20161
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 10163079
21.1%
I 4690319
9.7%
H 3396449
 
7.1%
C 3363484
 
7.0%
U 3305229
 
6.9%
S 3198663
 
6.6%
N 2746745
 
5.7%
Y 2428037
 
5.0%
G 2422501
 
5.0%
O 2299047
 
4.8%
Other values (13) 10135215
21.0%
None
ValueCountFrequency (%)
Ñ 20161
100.0%

CANTON
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
GUAYAQUIL
1534634 
DISTRITO METROPOLITANO DE QUITO
1148237 
MILAGRO
595648 
PORTOVIEJO
413746 
RUMIÑAHUI
275964 
Other values (73)
2607727 

Length

Max length31
Median length19
Mean length11.933109
Min length4

Characters and Unicode

Total characters78471600
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDISTRITO METROPOLITANO DE QUITO
2nd rowMILAGRO
3rd rowLOJA
4th rowLOJA
5th rowLOJA

Common Values

ValueCountFrequency (%)
GUAYAQUIL 1534634
23.3%
DISTRITO METROPOLITANO DE QUITO 1148237
17.5%
MILAGRO 595648
 
9.1%
PORTOVIEJO 413746
 
6.3%
RUMIÑAHUI 275964
 
4.2%
LOJA 267695
 
4.1%
CUENCA 266970
 
4.1%
RIOBAMBA 250415
 
3.8%
MACHALA 209321
 
3.2%
IBARRA 209249
 
3.2%
Other values (68) 1404077
21.4%

Length

2023-03-10T02:46:19.269646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guayaquil 1534634
14.9%
de 1168030
11.4%
quito 1148257
11.2%
distrito 1148237
11.2%
metropolitano 1148237
11.2%
milagro 595648
 
5.8%
portoviejo 413746
 
4.0%
rumiñahui 275964
 
2.7%
loja 267695
 
2.6%
cuenca 266970
 
2.6%
Other values (82) 2317804
22.5%

Most occurring characters

ValueCountFrequency (%)
A 9717085
12.4%
O 8965079
11.4%
I 8525273
10.9%
T 6900772
 
8.8%
U 5488676
 
7.0%
R 4548275
 
5.8%
L 4294863
 
5.5%
3709266
 
4.7%
E 3682344
 
4.7%
M 3057108
 
3.9%
Other values (15) 19582859
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 74762334
95.3%
Space Separator 3709266
 
4.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 9717085
13.0%
O 8965079
12.0%
I 8525273
11.4%
T 6900772
9.2%
U 5488676
 
7.3%
R 4548275
 
6.1%
L 4294863
 
5.7%
E 3682344
 
4.9%
M 3057108
 
4.1%
Q 2826440
 
3.8%
Other values (14) 16756419
22.4%
Space Separator
ValueCountFrequency (%)
3709266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74762334
95.3%
Common 3709266
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 9717085
13.0%
O 8965079
12.0%
I 8525273
11.4%
T 6900772
9.2%
U 5488676
 
7.3%
R 4548275
 
6.1%
L 4294863
 
5.7%
E 3682344
 
4.9%
M 3057108
 
4.1%
Q 2826440
 
3.8%
Other values (14) 16756419
22.4%
Common
ValueCountFrequency (%)
3709266
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78193109
99.6%
None 278491
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 9717085
12.4%
O 8965079
11.5%
I 8525273
10.9%
T 6900772
 
8.8%
U 5488676
 
7.0%
R 4548275
 
5.8%
L 4294863
 
5.5%
3709266
 
4.7%
E 3682344
 
4.7%
M 3057108
 
3.9%
Other values (14) 19304368
24.7%
None
ValueCountFrequency (%)
Ñ 278491
100.0%

PARROQUIA
Categorical

Distinct113
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
GUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL
1534108 
QUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR
1034223 
MILAGRO, CABECERA CANTONAL
595648 
PORTOVIEJO
413746 
SANGOLQUÍ
275437 
Other values (108)
2722794 

Length

Max length97
Median length49
Mean length42.106263
Min length4

Characters and Unicode

Total characters276888931
Distinct characters34
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR
2nd rowMILAGRO, CABECERA CANTONAL
3rd rowLOJA, CABECERA CANTONAL Y CAPITAL PROVINCIAL
4th rowLOJA, CABECERA CANTONAL Y CAPITAL PROVINCIAL
5th rowLOJA, CABECERA CANTONAL Y CAPITAL PROVINCIAL

Common Values

ValueCountFrequency (%)
GUAYAQUIL, CABECERA CANTONAL Y CAPITAL PROVINCIAL 1534108
23.3%
QUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADOR 1034223
15.7%
MILAGRO, CABECERA CANTONAL 595648
 
9.1%
PORTOVIEJO 413746
 
6.3%
SANGOLQUÍ 275437
 
4.2%
LOJA, CABECERA CANTONAL Y CAPITAL PROVINCIAL 267475
 
4.1%
CUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL. 260396
 
4.0%
RIOBAMBA, CABECERA CANTONAL Y CAPITAL PROVINCIAL 250264
 
3.8%
MACHALA 209247
 
3.2%
SAN MIGUEL DE IBARRA, CABECERA CANTONAL Y CAPITAL PROVINCIAL 209011
 
3.2%
Other values (103) 1526401
23.2%

Length

2023-03-10T02:46:19.325456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cabecera 4634515
12.9%
cantonal 4634515
12.9%
provincial 3886686
10.8%
y 3886686
10.8%
capital 3886686
10.8%
guayaquil 1534108
 
4.3%
la 1331267
 
3.7%
de 1296446
 
3.6%
quito 1034243
 
2.9%
distrito 1034223
 
2.9%
Other values (134) 8777337
24.4%

Most occurring characters

ValueCountFrequency (%)
A 43067194
15.6%
29360756
10.6%
C 25021728
9.0%
I 20614998
 
7.4%
L 20474575
 
7.4%
O 18300755
 
6.6%
E 16691669
 
6.0%
N 15578883
 
5.6%
R 14851407
 
5.4%
T 14771482
 
5.3%
Other values (24) 58155484
21.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 241581223
87.2%
Space Separator 29360756
 
10.6%
Other Punctuation 5929134
 
2.1%
Open Punctuation 8842
 
< 0.1%
Close Punctuation 8842
 
< 0.1%
Dash Punctuation 134
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 43067194
17.8%
C 25021728
10.4%
I 20614998
8.5%
L 20474575
8.5%
O 18300755
7.6%
E 16691669
 
6.9%
N 15578883
 
6.4%
R 14851407
 
6.1%
T 14771482
 
6.1%
P 10441957
 
4.3%
Other values (18) 41766575
17.3%
Other Punctuation
ValueCountFrequency (%)
, 5668738
95.6%
. 260396
 
4.4%
Space Separator
ValueCountFrequency (%)
29360756
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8842
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8842
100.0%
Dash Punctuation
ValueCountFrequency (%)
134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 241581223
87.2%
Common 35307708
 
12.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 43067194
17.8%
C 25021728
10.4%
I 20614998
8.5%
L 20474575
8.5%
O 18300755
7.6%
E 16691669
 
6.9%
N 15578883
 
6.4%
R 14851407
 
6.1%
T 14771482
 
6.1%
P 10441957
 
4.3%
Other values (18) 41766575
17.3%
Common
ValueCountFrequency (%)
29360756
83.2%
, 5668738
 
16.1%
. 260396
 
0.7%
( 8842
 
< 0.1%
) 8842
 
< 0.1%
134
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 276464802
99.8%
None 423995
 
0.2%
Punctuation 134
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 43067194
15.6%
29360756
10.6%
C 25021728
9.1%
I 20614998
 
7.5%
L 20474575
 
7.4%
O 18300755
 
6.6%
E 16691669
 
6.0%
N 15578883
 
5.6%
R 14851407
 
5.4%
T 14771482
 
5.3%
Other values (18) 57731355
20.9%
None
ValueCountFrequency (%)
Í 285093
67.2%
Ñ 103379
 
24.4%
Á 32898
 
7.8%
Ó 1478
 
0.3%
É 1147
 
0.3%
Punctuation
ValueCountFrequency (%)
134
100.0%
Distinct175
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
MATRIZ - GUAYAQUIL
1472238 
MATRIZ - QUITO
962724 
MATRIZ - MILAGRO
576897 
MATRIZ - PORTOVIEJO
412884 
MATRIZ - CAMPUS CENTRAL
 
278644
Other values (170)
2855007 

Length

Max length71
Median length42
Mean length16.526857
Min length4

Characters and Unicode

Total characters108389643
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMATRIZ - QUITO
2nd rowMATRIZ - MILAGRO
3rd rowMATRIZ - LOJA
4th rowMATRIZ - LOJA
5th rowMATRIZ - LOJA

Common Values

ValueCountFrequency (%)
MATRIZ - GUAYAQUIL 1472238
22.4%
MATRIZ - QUITO 962724
14.6%
MATRIZ - MILAGRO 576897
 
8.8%
MATRIZ - PORTOVIEJO 412884
 
6.3%
MATRIZ - CAMPUS CENTRAL 278644
 
4.2%
MATRIZ - LOJA 259307
 
3.9%
MATRIZ - AZUAY. 240948
 
3.7%
MATRIZ - RIOBAMBA 227095
 
3.5%
MATRIZ - MANTA 205128
 
3.1%
MATRIZ - IBARRA 204292
 
3.1%
Other values (165) 1718237
26.1%

Length

2023-03-10T02:46:19.385661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6182908
31.1%
matriz 6131638
30.9%
guayaquil 1533519
 
7.7%
quito 1017418
 
5.1%
milagro 592853
 
3.0%
portoviejo 413654
 
2.1%
campus 278644
 
1.4%
central 278644
 
1.4%
loja 267750
 
1.3%
riobamba 248834
 
1.3%
Other values (121) 2913162
14.7%

Most occurring characters

ValueCountFrequency (%)
A 15398605
14.2%
13308845
12.3%
I 10663945
9.8%
T 8709329
8.0%
R 8584857
7.9%
M 8029823
 
7.4%
Z 6411592
 
5.9%
- 6196591
 
5.7%
U 5156211
 
4.8%
O 4638772
 
4.3%
Other values (31) 21291073
19.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 88621088
81.8%
Space Separator 13308845
 
12.3%
Dash Punctuation 6196591
 
5.7%
Other Punctuation 248155
 
0.2%
Lowercase Letter 8030
 
< 0.1%
Close Punctuation 3467
 
< 0.1%
Open Punctuation 3467
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15398605
17.4%
I 10663945
12.0%
T 8709329
9.8%
R 8584857
9.7%
M 8029823
9.1%
Z 6411592
7.2%
U 5156211
 
5.8%
O 4638772
 
5.2%
L 3742631
 
4.2%
Q 2707045
 
3.1%
Other values (17) 14578278
16.5%
Lowercase Letter
ValueCountFrequency (%)
e 5180
64.5%
d 2650
33.0%
a 60
 
0.7%
i 40
 
0.5%
c 40
 
0.5%
n 20
 
0.2%
é 20
 
0.2%
m 20
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 244688
98.6%
, 3467
 
1.4%
Space Separator
ValueCountFrequency (%)
13308845
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6196591
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3467
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3467
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88629118
81.8%
Common 19760525
 
18.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15398605
17.4%
I 10663945
12.0%
T 8709329
9.8%
R 8584857
9.7%
M 8029823
9.1%
Z 6411592
7.2%
U 5156211
 
5.8%
O 4638772
 
5.2%
L 3742631
 
4.2%
Q 2707045
 
3.1%
Other values (25) 14586308
16.5%
Common
ValueCountFrequency (%)
13308845
67.4%
- 6196591
31.4%
. 244688
 
1.2%
) 3467
 
< 0.1%
, 3467
 
< 0.1%
( 3467
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108373646
> 99.9%
None 15997
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15398605
14.2%
13308845
12.3%
I 10663945
9.8%
T 8709329
8.0%
R 8584857
7.9%
M 8029823
 
7.4%
Z 6411592
 
5.9%
- 6196591
 
5.7%
U 5156211
 
4.8%
O 4638772
 
4.3%
Other values (27) 21275076
19.6%
None
ValueCountFrequency (%)
Ó 9970
62.3%
Ñ 3156
 
19.7%
Í 2851
 
17.8%
é 20
 
0.1%

PRD_ID_SEGMENTO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing17562
Missing (%)0.3%
Memory size50.2 MiB
800.0
6477717 
676.0
 
80677

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters32791970
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row800.0
2nd row800.0
3rd row800.0
4th row800.0
5th row800.0

Common Values

ValueCountFrequency (%)
800.0 6477717
98.5%
676.0 80677
 
1.2%
(Missing) 17562
 
0.3%

Length

2023-03-10T02:46:19.436335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:19.482773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
800.0 6477717
98.8%
676.0 80677
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 19513828
59.5%
. 6558394
 
20.0%
8 6477717
 
19.8%
6 161354
 
0.5%
7 80677
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26233576
80.0%
Other Punctuation 6558394
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19513828
74.4%
8 6477717
 
24.7%
6 161354
 
0.6%
7 80677
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 6558394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32791970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19513828
59.5%
. 6558394
 
20.0%
8 6477717
 
19.8%
6 161354
 
0.5%
7 80677
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32791970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19513828
59.5%
. 6558394
 
20.0%
8 6477717
 
19.8%
6 161354
 
0.5%
7 80677
 
0.2%

SEGMETO_CARRERA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
OFERTA PÚBLICA
6489822 
POLITICA DE ACCION AFIRMATIVA
 
80677
POBLACION GENERAL
 
5457

Length

Max length29
Median length14
Mean length14.186517
Min length14

Characters and Unicode

Total characters93289910
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOFERTA PÚBLICA
2nd rowOFERTA PÚBLICA
3rd rowOFERTA PÚBLICA
4th rowOFERTA PÚBLICA
5th rowOFERTA PÚBLICA

Common Values

ValueCountFrequency (%)
OFERTA PÚBLICA 6489822
98.7%
POLITICA DE ACCION AFIRMATIVA 80677
 
1.2%
POBLACION GENERAL 5457
 
0.1%

Length

2023-03-10T02:46:19.523698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-10T02:46:19.575659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
oferta 6489822
48.7%
pública 6489822
48.7%
politica 80677
 
0.6%
de 80677
 
0.6%
accion 80677
 
0.6%
afirmativa 80677
 
0.6%
poblacion 5457
 
< 0.1%
general 5457
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 13393943
14.4%
I 6898664
 
7.4%
6737310
 
7.2%
C 6737310
 
7.2%
O 6662090
 
7.1%
T 6651176
 
7.1%
E 6581413
 
7.1%
L 6581413
 
7.1%
R 6575956
 
7.0%
P 6575956
 
7.0%
Other values (8) 19894679
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 86552600
92.8%
Space Separator 6737310
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 13393943
15.5%
I 6898664
8.0%
C 6737310
7.8%
O 6662090
7.7%
T 6651176
7.7%
E 6581413
7.6%
L 6581413
7.6%
R 6575956
7.6%
P 6575956
7.6%
F 6570499
7.6%
Other values (7) 13324180
15.4%
Space Separator
ValueCountFrequency (%)
6737310
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86552600
92.8%
Common 6737310
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 13393943
15.5%
I 6898664
8.0%
C 6737310
7.8%
O 6662090
7.7%
T 6651176
7.7%
E 6581413
7.6%
L 6581413
7.6%
R 6575956
7.6%
P 6575956
7.6%
F 6570499
7.6%
Other values (7) 13324180
15.4%
Common
ValueCountFrequency (%)
6737310
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86800088
93.0%
None 6489822
 
7.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 13393943
15.4%
I 6898664
7.9%
6737310
7.8%
C 6737310
7.8%
O 6662090
7.7%
T 6651176
7.7%
E 6581413
7.6%
L 6581413
7.6%
R 6575956
7.6%
P 6575956
7.6%
Other values (7) 13404857
15.4%
None
ValueCountFrequency (%)
Ú 6489822
100.0%

cod_final
Real number (ℝ)

Distinct505868
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5875693 × 109
Minimum1.0000009 × 109
Maximum9.999701 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.2 MiB
2023-03-10T02:46:19.631659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000009 × 109
5-th percentile1.2353309 × 109
Q11.9671712 × 109
median2.3294514 × 109
Q32.5752612 × 109
95-th percentile6.5997014 × 109
Maximum9.999701 × 109
Range8.9997001 × 109
Interquartile range (IQR)6.0809009 × 108

Descriptive statistics

Standard deviation1.5393558 × 109
Coefficient of variation (CV)0.5949042
Kurtosis9.5742217
Mean2.5875693 × 109
Median Absolute Deviation (MAD)2.8190041 × 108
Skewness3.0699059
Sum1.7015742 × 1016
Variance2.3696164 × 1018
MonotonicityNot monotonic
2023-03-10T02:46:19.688488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2154521774 196
 
< 0.1%
2151881765 184
 
< 0.1%
2186391738 179
 
< 0.1%
2465590901 177
 
< 0.1%
2170491792 175
 
< 0.1%
2165561738 170
 
< 0.1%
2370820965 168
 
< 0.1%
2146491729 160
 
< 0.1%
2178341701 159
 
< 0.1%
2438500956 157
 
< 0.1%
Other values (505858) 6574231
> 99.9%
ValueCountFrequency (%)
1000000901 5
 
< 0.1%
1000011238 9
< 0.1%
1000011729 5
 
< 0.1%
1000030892 5
 
< 0.1%
1000050374 2
 
< 0.1%
1000051774 4
 
< 0.1%
1000070956 3
 
< 0.1%
1000080147 3
 
< 0.1%
1000090929 18
< 0.1%
1000100829 5
 
< 0.1%
ValueCountFrequency (%)
9999700965 15
< 0.1%
9999630929 12
< 0.1%
9999501765 14
< 0.1%
9999440129 9
< 0.1%
9999392356 2
 
< 0.1%
9999360901 5
 
< 0.1%
9999351383 5
 
< 0.1%
9999311210 11
< 0.1%
9999300983 3
 
< 0.1%
9999291383 4
 
< 0.1%

archivo
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.2 MiB
primera_postulacion_per22.csv
861282 
primera_postulacion_per19.csv
744253 
primera_postulacion_per21.csv
706982 
primera_postulacion_per20.csv
699663 
primera_postulacion_per18.csv
585182 
Other values (16)
2978594 

Length

Max length32
Median length29
Mean length28.989597
Min length28

Characters and Unicode

Total characters190634314
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtercera_postulacion_per22.csv
2nd rowtercera_postulacion_per22.csv
3rd rowtercera_postulacion_per22.csv
4th rowtercera_postulacion_per22.csv
5th rowtercera_postulacion_per22.csv

Common Values

ValueCountFrequency (%)
primera_postulacion_per22.csv 861282
13.1%
primera_postulacion_per19.csv 744253
11.3%
primera_postulacion_per21.csv 706982
10.8%
primera_postulacion_per20.csv 699663
10.6%
primera_postulacion_per18.csv 585182
8.9%
segunda_postulacion_per22.csv 404773
 
6.2%
segunda_postulacion_per19.csv 400010
 
6.1%
segunda_postulacion_per21.csv 366405
 
5.6%
segunda_postulacion_per20.csv 333697
 
5.1%
segunda_postulacion_per18.csv 297845
 
4.5%
Other values (11) 1175864
17.9%

Length

2023-03-10T02:46:19.743321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
primera_postulacion_per22.csv 861282
13.1%
primera_postulacion_per19.csv 744253
11.3%
primera_postulacion_per21.csv 706982
10.8%
primera_postulacion_per20.csv 699663
10.6%
primera_postulacion_per18.csv 585182
8.9%
segunda_postulacion_per22.csv 404773
 
6.2%
segunda_postulacion_per19.csv 400010
 
6.1%
segunda_postulacion_per21.csv 366405
 
5.6%
segunda_postulacion_per20.csv 333697
 
5.1%
segunda_postulacion_per18.csv 297845
 
4.5%
Other values (11) 1175864
17.9%

Most occurring characters

ValueCountFrequency (%)
p 16748589
 
8.8%
r 15999939
 
8.4%
s 14954642
 
7.8%
c 14308841
 
7.5%
e 14052515
 
7.4%
a 13306074
 
7.0%
_ 13169474
 
6.9%
o 13133665
 
6.9%
i 10176061
 
5.3%
u 8532851
 
4.5%
Other values (14) 56251663
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157736972
82.7%
Connector Punctuation 13169474
 
6.9%
Decimal Number 13151912
 
6.9%
Other Punctuation 6575956
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 16748589
10.6%
r 15999939
10.1%
s 14954642
9.5%
c 14308841
9.1%
e 14052515
8.9%
a 13306074
8.4%
o 13133665
8.3%
i 10176061
 
6.5%
u 8532851
 
5.4%
n 8363867
 
5.3%
Other values (7) 28159928
17.9%
Decimal Number
ValueCountFrequency (%)
2 5750067
43.7%
1 3755201
28.6%
9 1401656
 
10.7%
0 1200308
 
9.1%
8 1044680
 
7.9%
Connector Punctuation
ValueCountFrequency (%)
_ 13169474
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6575956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 157736972
82.7%
Common 32897342
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 16748589
10.6%
r 15999939
10.1%
s 14954642
9.5%
c 14308841
9.1%
e 14052515
8.9%
a 13306074
8.4%
o 13133665
8.3%
i 10176061
 
6.5%
u 8532851
 
5.4%
n 8363867
 
5.3%
Other values (7) 28159928
17.9%
Common
ValueCountFrequency (%)
_ 13169474
40.0%
. 6575956
20.0%
2 5750067
17.5%
1 3755201
 
11.4%
9 1401656
 
4.3%
0 1200308
 
3.6%
8 1044680
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190634314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 16748589
 
8.8%
r 15999939
 
8.4%
s 14954642
 
7.8%
c 14308841
 
7.5%
e 14052515
 
7.4%
a 13306074
 
7.0%
_ 13169474
 
6.9%
o 13133665
 
6.9%
i 10176061
 
5.3%
u 8532851
 
4.5%
Other values (14) 56251663
29.5%

Interactions

2023-03-10T02:44:37.541958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:17.067395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:25.675020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:34.139791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:42.875176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:51.389478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:59.833942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:08.651158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:17.265230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:25.505000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:34.637458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:44.174019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:53.074678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:01.901113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:11.642321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:19.962026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:28.650646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:38.060349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:17.600636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:26.149629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:34.626239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:43.374512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:51.867683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:00.314880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:09.158653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:17.735717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:26.071240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:35.214676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:44.692029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:53.594392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:02.577857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:12.144208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:20.474944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:29.135657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:38.569219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:18.085642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:26.632794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:35.105955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:43.857580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:52.354650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:00.801682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:09.657708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:18.224872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:26.593642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:35.753290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:45.201456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:54.099547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:03.131695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:12.626962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:21.003706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:29.618060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:39.092590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:18.583246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:27.127141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:35.584254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:44.324143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:52.830592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:01.286252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:10.164456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:18.704610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:27.111802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:36.285612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:45.731401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:54.631391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:03.664771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:13.119868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:21.512223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:30.100767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:39.689667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:19.085104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:27.617096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:36.113242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:44.823188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:53.289158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:02.002957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:10.663952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:19.214321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:27.712031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:36.909127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:46.268627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:55.171631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:04.273076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:13.617515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:22.017493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:30.582257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:40.206350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:19.587579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:28.104421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:36.613365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:45.327985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:53.764891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:02.464986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:11.158629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:19.695250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:28.254163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:37.504165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:46.788756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:55.695949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:04.815301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:14.107250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:22.528651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:31.072764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:40.704664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:20.053537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:28.585802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:37.106693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:45.819610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:54.233414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:02.945282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:11.617293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:20.178672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:28.761365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:38.083390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:47.306419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:56.192473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:05.346865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:14.600706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:23.044963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:31.544715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:41.302628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:20.560306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:29.137141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:37.705836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:46.329482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:54.740569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:03.449798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:12.135232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:20.635685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:29.293223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:38.696281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:47.832083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:56.699967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:05.992391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:15.082051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:23.569624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:32.065881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:41.799444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:21.040681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:29.645048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:38.226641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:46.833856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:55.237314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:03.958577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:12.650047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:21.128211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:29.764152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:39.230711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:48.369539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:57.198462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:06.585291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:15.582812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:24.086353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:32.637018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:42.335194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:21.525352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:30.157945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:38.758626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:47.330274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:55.743676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:04.474709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:13.153408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:21.610713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:30.412749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:39.742495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:48.884542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:57.698142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:07.156067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:16.082106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:24.608935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:33.159757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:42.847056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:22.016640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:30.652965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:39.272363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:47.825936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:56.234636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:04.991908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:13.658358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:22.087266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:30.921656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:40.285832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:49.373333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:58.208569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:07.738814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:16.554581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:25.104354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:33.806729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:43.390959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:22.531889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:31.154062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:39.811551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:48.344245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:56.731187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:05.496629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:14.183640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:22.572263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:31.457470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:40.868421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:49.899070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:58.681529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:08.335507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:17.041799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:25.624932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:34.321355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:43.889355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:23.008351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:31.665562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:40.333702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:48.846227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:57.299650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:06.074876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:14.697601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:23.038276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:31.955770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:41.407114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:50.426379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:59.181145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:08.849467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:17.530179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:26.131507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:34.870052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:44.399168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:23.500644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:32.134937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:40.820953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:49.320724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:57.807818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:06.576263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:15.183249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:23.534659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:32.474754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:41.955330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:50.930711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:59.728236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:09.388670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:17.980725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:26.646417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:35.375841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:44.903815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:24.007177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:32.619358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:41.327383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:49.804345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:58.294934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:07.070845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:15.675829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:23.998658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:32.986368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:42.495806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:51.433072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:00.248333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:09.933776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:18.453286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:27.120434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:35.880415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:45.408736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:24.587932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:33.105881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:41.825810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:50.279505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:58.784269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:07.593791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:16.166880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:24.476383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:33.506188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:43.035168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:51.932515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:00.765748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:10.492846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:18.938209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:27.630986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:36.353751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:45.917637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:25.162078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:33.640138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:42.356716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:50.779491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:42:59.334201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:08.108474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:16.672254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:25.005975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:34.104714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:43.631215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:43:52.451677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:01.372127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:11.126474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:19.435144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:28.139849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-10T02:44:36.872428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-10T02:46:19.841548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Unnamed: 0INS_IDINI_IDCAE_NOTA_POSTULAPOS_IDCUS_IDNOTA_POSTULAPRD_ID_NUM_POSTULACIONIES_IDOFA_IDAPC_IDCCP_IDCAR_IDMODALIDAD_IDAREA_IDSUBAREA_IDcod_finalPER_IDINS_POBLACIONINS_TIPO_INSCRIPCIONSEGMENTO_ASPIRANTECAE_GRUPOPOS_PRIORIDADPOS_ESTADOIES_TIPO_IESIES_TIPO_FINANCIAMIENTOIES_ESTADOMODALIDADJORNADA_IDJORNADANIVELAREA_NOMBRESUBAREA_NOMBREPROVINCIACANTONPRD_ID_SEGMENTOSEGMETO_CARRERAarchivo
Unnamed: 01.0000.0610.0610.116-0.033-0.0240.121-0.536-0.0430.0700.0620.083-0.027-0.0270.0500.0040.0490.1380.0620.0700.0610.0690.0170.0050.0290.0180.0080.0330.1100.1100.0330.0400.0460.2290.2330.0320.0450.252
INS_ID0.0611.0001.0000.2070.9620.9580.2150.041-0.0320.9580.9580.9550.0050.095-0.0070.0310.2860.9250.2060.6380.1490.3310.0160.0120.0370.0190.0170.0730.1340.1330.1530.0380.0420.1170.1260.0800.0650.618
INI_ID0.0611.0001.0000.2080.9620.9580.2150.040-0.0320.9580.9580.9550.0050.095-0.0070.0310.2870.9240.1980.5700.1640.3350.0160.0120.0350.0230.0160.0720.1350.1350.1530.0380.0420.1200.1290.0830.0660.617
CAE_NOTA_POSTULA0.1160.2070.2081.0000.1860.2000.995-0.157-0.1810.2280.2260.251-0.089-0.0870.0500.0620.1850.1760.0710.1050.1270.1380.0160.0060.1450.0200.0050.0550.1300.1300.0970.0930.1030.1070.1110.0100.0100.136
POS_ID-0.0330.9620.9620.1861.0000.9920.1950.219-0.0420.9580.9580.9570.0150.101-0.0130.0270.3050.8730.1860.0930.1400.3160.0250.0160.0280.0320.0200.0730.1100.1090.1440.0410.0450.0860.1000.0790.0770.800
CUS_ID-0.0240.9580.9580.2000.9921.0000.2080.221-0.0620.9580.9580.9570.0100.103-0.0130.0320.3100.9280.1860.1030.1400.3470.0250.0160.1120.0470.0180.0810.1220.1220.1710.0500.0560.1300.1450.1070.1070.893
NOTA_POSTULA0.1210.2150.2150.9950.1950.2081.000-0.154-0.1740.2360.2340.257-0.089-0.0850.0490.0620.1880.1810.0690.1020.1250.1400.0160.0060.1430.0200.0050.0550.1260.1260.0950.0930.1020.1050.1100.0100.0100.137
PRD_ID_NUM_POSTULACION-0.5360.0410.040-0.1570.2190.221-0.1541.0000.0060.0380.0400.0330.0600.079-0.053-0.025-0.0190.1230.0570.0070.0670.0860.0670.0000.0050.0220.0010.0430.0710.0430.0370.0520.0570.0640.0870.0390.4521.000
IES_ID-0.043-0.032-0.032-0.181-0.042-0.062-0.1740.0061.000-0.044-0.036-0.0800.1560.109-0.049-0.028-0.0380.0220.0220.0250.0210.0280.0140.0020.9480.4710.0190.1690.1690.1700.4660.1670.2350.3020.4980.3470.2420.055
OFA_ID0.0700.9580.9580.2280.9580.9580.2360.038-0.0441.0000.9580.958-0.0050.098-0.0020.0360.3140.8440.1740.1030.1340.4230.0170.0120.0570.0240.0150.0730.1070.1060.1660.0580.0770.1130.1350.0860.0670.689
APC_ID0.0620.9580.9580.2260.9580.9580.2340.040-0.0360.9581.0000.9570.0120.0940.0030.0310.3140.9010.1770.1040.1330.3920.0160.0120.0570.0210.0190.0730.1110.1110.1630.0700.0860.1160.1330.0780.0780.682
CCP_ID0.0830.9550.9550.2510.9570.9570.2570.033-0.0800.9580.9571.000-0.0050.0700.0030.0430.3230.9540.2160.1350.1460.3480.0160.0120.1700.0520.0250.1310.2320.2310.2070.0670.0780.2710.3390.0870.0710.637
CAR_ID-0.0270.0050.005-0.0890.0150.010-0.0890.0600.156-0.0050.012-0.0051.000-0.016-0.027-0.004-0.0110.0270.0220.0240.0210.0240.0210.0020.5300.1040.0430.1140.1410.1400.2980.3690.5190.1350.2060.0840.0600.050
MODALIDAD_ID-0.0270.0950.095-0.0870.1010.103-0.0850.0790.1090.0980.0940.070-0.0161.0000.0090.1060.0160.0850.0900.0400.0250.0650.0210.0010.0170.0570.0031.0000.5770.5770.0200.2840.3140.2170.4630.0550.0550.116
AREA_ID0.050-0.007-0.0070.050-0.013-0.0130.049-0.053-0.049-0.0020.0030.003-0.0270.0091.0000.2230.0130.0420.0710.0420.0190.0300.0260.0020.2850.0950.0100.2290.2440.2440.1521.0000.8770.1180.1950.0730.0730.065
SUBAREA_ID0.0040.0310.0310.0620.0270.0320.062-0.025-0.0280.0360.0310.043-0.0040.1060.2231.0000.0060.0280.0470.0180.0060.0250.0120.0010.1570.0220.0040.1560.1440.1440.1210.7750.9960.1100.1990.0120.0120.049
cod_final0.0490.2860.2870.1850.3050.3100.188-0.019-0.0380.3140.3140.323-0.0110.0160.0130.0061.0000.0450.3280.2110.0260.0300.0040.0010.0310.0100.0030.0610.0820.0820.0170.0330.0350.0400.0460.0080.0060.032
PER_ID0.1380.9250.9240.1760.8730.9280.1810.1230.0220.8440.9010.9540.0270.0850.0420.0280.0451.0000.1880.1070.1520.5240.0160.0120.0350.0180.0170.0750.1230.1220.1610.0520.0580.1620.1750.0830.0671.000
INS_POBLACION0.0620.2060.1980.0710.1860.1860.0690.0570.0220.1740.1770.2160.0220.0900.0710.0470.3280.1881.0001.0000.0760.0960.0020.0050.0270.0060.0040.1060.1200.1200.0340.0750.0780.0700.0890.0130.0130.189
INS_TIPO_INSCRIPCION0.0700.6380.5700.1050.0930.1030.1020.0070.0250.1030.1040.1350.0240.0400.0420.0180.2110.1071.0001.0000.1190.1360.0020.0010.0220.0150.0020.0410.0800.0800.0210.0460.0510.1140.1190.0210.0210.116
SEGMENTO_ASPIRANTE0.0610.1490.1640.1270.1400.1400.1250.0670.0210.1340.1330.1460.0210.0250.0190.0060.0260.1520.0760.1191.0000.8660.0050.0010.0100.0570.0010.0230.0310.0310.0120.0270.0320.0890.0970.1330.1330.154
CAE_GRUPO0.0690.3310.3350.1380.3160.3470.1400.0860.0280.4230.3920.3480.0240.0650.0300.0250.0300.5240.0960.1360.8661.0000.0070.0120.0330.0680.0040.0480.0670.0670.1620.0280.0260.0570.0630.1490.1490.271
POS_PRIORIDAD0.0170.0160.0160.0160.0250.0250.0160.0670.0140.0170.0160.0160.0210.0210.0260.0120.0040.0160.0020.0020.0050.0071.0000.0000.0170.0080.0020.0190.0170.0170.0100.0280.0460.0200.0290.0060.0370.068
POS_ESTADO0.0050.0120.0120.0060.0160.0160.0060.0000.0020.0120.0120.0120.0020.0010.0020.0010.0010.0120.0050.0010.0010.0120.0001.0000.0010.0010.0000.0010.0020.0020.0050.0030.0060.0050.0060.0010.0010.017
IES_TIPO_IES0.0290.0370.0350.1450.0280.1120.1430.0050.9480.0570.0570.1700.5300.0170.2850.1570.0310.0350.0270.0220.0100.0330.0170.0011.0000.1980.0020.3590.3380.3380.9710.3100.4590.2130.4030.0920.0920.061
IES_TIPO_FINANCIAMIENTO0.0180.0190.0230.0200.0320.0470.0200.0220.4710.0240.0210.0520.1040.0570.0950.0220.0100.0180.0060.0150.0570.0680.0080.0010.1981.0000.0120.0930.0950.0950.1650.1270.1310.0890.1670.7640.7640.054
IES_ESTADO0.0080.0170.0160.0050.0200.0180.0050.0010.0190.0150.0190.0250.0430.0030.0100.0040.0030.0170.0040.0020.0010.0040.0020.0000.0020.0121.0000.0660.0170.0170.0030.0160.0210.0150.0170.0010.0010.029
MODALIDAD0.0330.0730.0720.0550.0730.0810.0550.0430.1690.0730.0730.1310.1141.0000.2290.1560.0610.0750.1060.0410.0230.0480.0190.0010.3590.0930.0661.0000.5000.5000.1780.2460.3400.2270.3800.0800.0560.091
JORNADA_ID0.1100.1340.1350.1300.1100.1220.1260.0710.1690.1070.1110.2320.1410.5770.2440.1440.0820.1230.1200.0800.0310.0670.0170.0020.3380.0950.0170.5001.0001.0000.1650.2610.2760.4190.5450.0830.0830.140
JORNADA0.1100.1330.1350.1300.1090.1220.1260.0430.1700.1060.1110.2310.1400.5770.2440.1440.0820.1220.1200.0800.0310.0670.0170.0020.3380.0950.0170.5001.0001.0000.1650.2610.2760.4190.5450.0830.0590.142
NIVEL0.0330.1530.1530.0970.1440.1710.0950.0370.4660.1660.1630.2070.2980.0200.1520.1210.0170.1610.0340.0210.0120.1620.0100.0050.9710.1650.0030.1780.1650.1651.0000.1830.2610.1190.2120.1170.0840.169
AREA_NOMBRE0.0400.0380.0380.0930.0410.0500.0930.0520.1670.0580.0700.0670.3690.2841.0000.7750.0330.0520.0750.0460.0270.0280.0280.0030.3100.1270.0160.2460.2610.2610.1831.0000.9050.1120.1740.0970.0970.049
SUBAREA_NOMBRE0.0460.0420.0420.1030.0450.0560.1020.0570.2350.0770.0860.0780.5190.3140.8770.9960.0350.0580.0780.0510.0320.0260.0460.0060.4590.1310.0210.3400.2760.2760.2610.9051.0000.1310.1790.0940.0940.053
PROVINCIA0.2290.1170.1200.1070.0860.1300.1050.0640.3020.1130.1160.2710.1350.2170.1180.1100.0400.1620.0700.1140.0890.0570.0200.0050.2130.0890.0150.2270.4190.4190.1190.1120.1311.0001.0000.0930.0950.084
CANTON0.2330.1260.1290.1110.1000.1450.1100.0870.4980.1350.1330.3390.2060.4630.1950.1990.0460.1750.0890.1190.0970.0630.0290.0060.4030.1670.0170.3800.5450.5450.2120.1740.1791.0001.0000.1970.1620.100
PRD_ID_SEGMENTO0.0320.0800.0830.0100.0790.1070.0100.0390.3470.0860.0780.0870.0840.0550.0730.0120.0080.0830.0130.0210.1330.1490.0060.0010.0920.7640.0010.0800.0830.0830.1170.0970.0940.0930.1971.0001.0000.093
SEGMETO_CARRERA0.0450.0650.0660.0100.0770.1070.0100.4520.2420.0670.0780.0710.0600.0550.0730.0120.0060.0670.0130.0210.1330.1490.0370.0010.0920.7640.0010.0560.0830.0590.0840.0970.0940.0950.1621.0001.0000.710
archivo0.2520.6180.6170.1360.8000.8930.1371.0000.0550.6890.6820.6370.0500.1160.0650.0490.0321.0000.1890.1160.1540.2710.0680.0170.0610.0540.0290.0910.1400.1420.1690.0490.0530.0840.1000.0930.7101.000

Missing values

2023-03-10T02:44:49.004805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-10T02:45:07.306619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-10T02:46:00.353399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0INS_IDINI_IDPER_IDINS_POBLACIONINS_TIPO_INSCRIPCIONSEGMENTO_ASPIRANTECAE_GRUPOCAE_ESTADOCAE_NOTA_POSTULAPOS_IDPOS_FECHA_POSTULACIONCUS_IDNOTA_POSTULAPRD_ID_NUM_POSTULACIONPOS_PRIORIDADPOS_ESTADOIES_IDIES_NOMBRE_INSTITIES_TIPO_IESIES_TIPO_FINANCIAMIENTOOFA_IDIES_ESTADOAPC_IDCCP_IDCAR_IDCAR_NOMBRE_CARRERAMODALIDAD_IDMODALIDADJORNADA_IDJORNADANIVELAREA_IDAREA_NOMBRESUBAREA_IDSUBAREA_NOMBREPROVINCIACANTONPARROQUIACAM_NOMBRE_CAMPUSPRD_ID_SEGMENTOSEGMETO_CARRERAcod_finalarchivo
0112263583.07459572.022NaN3.0POBLACION GENERALPOBLACION GENERAL1.0714.029103938.027/10/2021 15:27311701.0714.049421.046UNIVERSIDAD CENTRAL DEL ECUADORUPÚBLICA177772A45466.0319795455ADMINISTRACION DE EMPRESAS8.0DISTANCIA3.0NO APLICA JORNADATERCER NIVEL9.0ADMINISTRACION500.0EDUCACION COMERCIAL Y ADMINISTRACIONPICHINCHADISTRITO METROPOLITANO DE QUITOQUITO DISTRITO METROPOLITANO, CABECERA CANTONAL, CAPITAL PROVINCIAL Y DE LA REPUBLICA DEL ECUADORMATRIZ - QUITO800.0OFERTA PÚBLICA4896670101tercera_postulacion_per22.csv
1212263583.07459572.022NaN3.0POBLACION GENERALPOBLACION GENERAL1.0714.029103941.027/10/2021 15:27311785.0714.049451.059UNIVERSIDAD ESTATAL DE MILAGROUPÚBLICA174130A47779.0301945205COMUNICACION441.0EN LINEA3.0NO APLICA JORNADATERCER NIVEL13.0CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO15.0PERIODISMO E INFORMACIONGUAYASMILAGROMILAGRO, CABECERA CANTONALMATRIZ - MILAGRO800.0OFERTA PÚBLICA4896670101tercera_postulacion_per22.csv
2312263583.07459572.022NaN3.0POBLACION GENERALPOBLACION GENERAL1.0714.029103939.027/10/2021 15:27312325.0714.049431.072UNIVERSIDAD NACIONAL DE LOJAUPÚBLICA175203A45847.0297484781DERECHO8.0DISTANCIA3.0NO APLICA JORNADATERCER NIVEL13.0CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO510.0DERECHOLOJALOJALOJA, CABECERA CANTONAL Y CAPITAL PROVINCIALMATRIZ - LOJA800.0OFERTA PÚBLICA4896670101tercera_postulacion_per22.csv
3412263583.07459572.022NaN3.0POBLACION GENERALPOBLACION GENERAL1.0714.029103937.027/10/2021 15:27312048.0714.049411.072UNIVERSIDAD NACIONAL DE LOJAUPÚBLICA180652A48104.0297435205COMUNICACION8.0DISTANCIA3.0NO APLICA JORNADATERCER NIVEL13.0CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO15.0PERIODISMO E INFORMACIONLOJALOJALOJA, CABECERA CANTONAL Y CAPITAL PROVINCIALMATRIZ - LOJA800.0OFERTA PÚBLICA4896670101tercera_postulacion_per22.csv
4512263583.07459572.022NaN3.0POBLACION GENERALPOBLACION GENERAL1.0714.029103940.027/10/2021 15:27312305.0714.049441.072UNIVERSIDAD NACIONAL DE LOJAUPÚBLICA177913A47704.0297544473EDUCACION INICIAL8.0DISTANCIA3.0NO APLICA JORNADATERCER NIVEL14.0EDUCACION511.0EDUCACIONLOJALOJALOJA, CABECERA CANTONAL Y CAPITAL PROVINCIALMATRIZ - LOJA800.0OFERTA PÚBLICA4896670101tercera_postulacion_per22.csv
5611563446.06904301.022No escolar1.0POLITICA DE ACCION AFIRMATIVA/POLITICA DE ACCION AFIRMATIVA1.0774.029060047.027/10/2021 13:13312155.0774.049411.048UNIVERSIDAD DE CUENCAUPÚBLICA177897A45489.0317665087DISEÑO DE INTERIORES9.0PRESENCIAL1.0INTENSIVATERCER NIVEL11.0ARTES Y HUMANIDADES504.0ARTESAZUAYCUENCACUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.MATRIZ - AZUAY.800.0OFERTA PÚBLICA1817840156tercera_postulacion_per22.csv
6711563446.06904301.022No escolar1.0POLITICA DE ACCION AFIRMATIVA/POLITICA DE ACCION AFIRMATIVA1.0774.029060049.027/10/2021 13:13311927.0774.049431.048UNIVERSIDAD DE CUENCAUPÚBLICA179112A46911.0317675167DISEÑO GRAFICO9.0PRESENCIAL1.0INTENSIVATERCER NIVEL11.0ARTES Y HUMANIDADES504.0ARTESAZUAYCUENCACUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.MATRIZ - AZUAY.800.0OFERTA PÚBLICA1817840156tercera_postulacion_per22.csv
7811563446.06904301.022No escolar1.0POLITICA DE ACCION AFIRMATIVA/POLITICA DE ACCION AFIRMATIVA1.0774.029060048.027/10/2021 13:13312375.0774.049421.048UNIVERSIDAD DE CUENCAUPÚBLICA172459A45840.0317715029ELECTRICIDAD9.0PRESENCIAL1.0INTENSIVATERCER NIVEL26.0INGENIERIA, INDUSTRIA Y CONSTRUCCION63.0INGENIERIA Y PROFESIONES AFINESAZUAYCUENCACUENCA, CABECERA CANTONAL Y CAPITAL PROVINCIAL.MATRIZ - AZUAY.800.0OFERTA PÚBLICA1817840156tercera_postulacion_per22.csv
8911704080.06974616.022NaN1.0POBLACION GENERALPOBLACION GENERAL1.0754.029251832.028/10/2021 10:24312319.0754.049411.086UNIVERSIDAD TECNICA DE MANABIUPÚBLICA179253A45494.0292844781DERECHO441.0EN LINEA3.0NO APLICA JORNADATERCER NIVEL13.0CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO510.0DERECHOMANABIPORTOVIEJOPORTOVIEJOMATRIZ - PORTOVIEJO800.0OFERTA PÚBLICA2734800183tercera_postulacion_per22.csv
91011704080.06974616.022NaN1.0POBLACION GENERALPOBLACION GENERAL1.0754.029251833.028/10/2021 10:24312503.0754.049421.059UNIVERSIDAD ESTATAL DE MILAGROUPÚBLICA176762A47778.0301974781DERECHO441.0EN LINEA3.0NO APLICA JORNADATERCER NIVEL13.0CIENCIAS SOCIALES, PERIODISMO, INFORMACION Y DERECHO510.0DERECHOGUAYASMILAGROMILAGRO, CABECERA CANTONALMATRIZ - MILAGRO800.0OFERTA PÚBLICA2734800183tercera_postulacion_per22.csv
Unnamed: 0INS_IDINI_IDPER_IDINS_POBLACIONINS_TIPO_INSCRIPCIONSEGMENTO_ASPIRANTECAE_GRUPOCAE_ESTADOCAE_NOTA_POSTULAPOS_IDPOS_FECHA_POSTULACIONCUS_IDNOTA_POSTULAPRD_ID_NUM_POSTULACIONPOS_PRIORIDADPOS_ESTADOIES_IDIES_NOMBRE_INSTITIES_TIPO_IESIES_TIPO_FINANCIAMIENTOOFA_IDIES_ESTADOAPC_IDCCP_IDCAR_IDCAR_NOMBRE_CARRERAMODALIDAD_IDMODALIDADJORNADA_IDJORNADANIVELAREA_IDAREA_NOMBRESUBAREA_IDSUBAREA_NOMBREPROVINCIACANTONPARROQUIACAM_NOMBRE_CAMPUSPRD_ID_SEGMENTOSEGMETO_CARRERAcod_finalarchivo
65759466767775969.04406230.018NaNNaNNaNNaNNaNNaN18511721.0NaNNaNNaN5611NaN22NaNNaNNaN103621NaNNaN204134911NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL1742482301cuarta_asigna_directa_per18.csv
65759476777954502.04643575.018NaNNaNNaNNaNNaNNaN18511324.0NaNNaNNaN5611NaN22NaNNaNNaN103621NaNNaN204134911NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL2003922310cuarta_asigna_directa_per18.csv
65759486787779622.04419043.018NaNNaNNaNNaNNaNNaN18511726.0NaNNaNNaN5611NaN22NaNNaNNaN102184NaNNaN204114618NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL1748602338cuarta_asigna_directa_per18.csv
65759496797946715.04580788.018NaNNaNNaNNaNNaNNaN18511428.0NaNNaNNaN5611NaN22NaNNaNNaN95017NaNNaN204034473NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL2100392356cuarta_asigna_directa_per18.csv
65759506807473282.04174454.018NaNNaNNaNNaNNaNNaN18511389.0NaNNaNNaN5611NaN493NaNNaNNaN99593NaNNaN201387100NaNNaNPRESENCIALNaNMATUTINATECNOLOGICO SUPERIORNaNNaNNaNNaNSANTO DOMINGO DE LOS TSACHILASSANTO DOMINGOSANTO DOMINGO DE LOS COLORADOSNaNNaNPOBLACION GENERAL2067762374cuarta_asigna_directa_per18.csv
65759516817493865.04184775.018NaNNaNNaNNaNNaNNaN18511460.0NaNNaNNaN5611NaN30NaNNaNNaN98801NaNNaN198964533NaNNaNPRESENCIALNaNNOCTURNATERCER NIVELNaNNaNNaNNaNMANABIBOLIVARCALCETANaNNaNPOBLACION GENERAL1320052301cuarta_asigna_directa_per18.csv
65759526827749169.04479372.018NaNNaNNaNNaNNaNNaN18511504.0NaNNaNNaN5611NaN22NaNNaNNaN95017NaNNaN204034473NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL1695572383cuarta_asigna_directa_per18.csv
65759536837794139.04449302.018NaNNaNNaNNaNNaNNaN18511405.0NaNNaNNaN5611NaN22NaNNaNNaN103621NaNNaN204134911NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL1772182365cuarta_asigna_directa_per18.csv
65759546847797070.04431939.018NaNNaNNaNNaNNaNNaN18511635.0NaNNaNNaN5611NaN22NaNNaNNaN95017NaNNaN204034473NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNPICHINCHARUMIÑAHUISANGOLQUÍNaNNaNPOBLACION GENERAL1776772310cuarta_asigna_directa_per18.csv
65759556857309997.04092839.018NaNNaNNaNNaNNaNNaN18511156.0NaNNaNNaN5611NaN59NaNNaNNaN99032NaNNaN208974618NaNNaNEN LINEANaNNO APLICA JORNADATERCER NIVELNaNNaNNaNNaNGUAYASMILAGROMILAGRO, CABECERA CANTONALNaNNaNPOBLACION GENERAL4884592474cuarta_asigna_directa_per18.csv